Electronic copy available at: https://ssrn.com/abstract=2816207

Mortgage Default Risk:New Evidence From Internet Search Queries∗

Marcelle Chauvet†

University of California, Riverside

Stuart Gabriel‡

University of California, Los Angeles

Chandler Lutz§

Copenhagen Business School

Forthcoming in Journal of Urban Economics

November 7, 2016

Abstract

We use Google search query data to develop a broad-based and real-time index

of mortgage default risk. Unlike established indicators, our Mortgage Default Risk

Index (MDRI) directly reflects households’ concerns regarding their risk of mortgage

default. The MDRI predicts housing returns, mortgage delinquency indicators, and

subprime credit default swaps. These results persist both in- and out-of-sample and at

multiple data frequencies. Together, research findings suggest internet search queries

yield valuable new insights into household mortgage default risk.

JEL Classification: R30, G02, G12 ;

Keywords: Mortgage Default Risk

∗We would like to thank Brian Bucks, Ed Glaeser, Aurel Hizmo, Ed Leamer, Selale Tuzel, Cindy Soo,Todd Sinai, Seth Stephans-Davidowitz, Hal Varian, two anonymous referees, an editor, and conference par-ticipants at the American Economic Association, the NBER, the Summer Real Estate Symposium (at theWFA), the UCLA/Federal Reserve Bank of San Francisco Conference on Housing and the Macroeconomy,the UCLA Finance Department, the AREUEA International Conference, IDC Herzeliya, Tel Aviv University,and Copenhagen Business School for their helpful comments.†Marcelle Chauvet can be contacted at chauvet@ucr.edu‡Stuart Gabriel can be contacted at stuart.gabriel@anderson.ucla.edu§Chandler Lutz can be contacted at cl.eco@cbs.dk

Electronic copy available at: https://ssrn.com/abstract=2816207

1 Introduction

The financial concerns of homeowners are of paramount importance to the US economy.1 As

evidenced during the 2000s housing and financial crisis, elevated mortgage delinquencies and

defaults can dampen future house prices, exacerbate episodes of pessimism among consumers

and investors, and wreak havoc on the macroeconomy and financial markets. While aggregate

financial risk was captured by an array of generalized market indices such as the VIX index

(the so-called “stock market fear index” as proxied by the expected variance of S&P500 stock

returns), none of these measures provided timely insights specific to mortgage default risk

during run-up to the crisis. Further, the few available measures of mortgage default risk only

captured information known to lenders or financial market participants and thus neglected

potentially sensitive information on mortgage distress emanating directly from households.

The paucity of information on household mortgage concerns was striking, given the leading

role of housing in the global downturn.

In this paper, we aim to fill this gap via the development and predictive test of a new and

direct measure of mortgage default risk. We seek a broad-based, real-time gauge capable of

capturing the marked swings in default risk that pervaded housing markets over recent years.

In developing and testing such an index, we apply new, internet search query information

from Google. Google search queries can be tabulated in “real-time” (Choi and Varian (2012))

at multiple frequencies. In contrast, popular housing indices from Case-Shiller, for example,

are only available with a 2 month lag, while other potential drivers of mortgage default, such

as unemployment rates, have little predictive power (Gyourko and Tracy (2014)).

Using Google data, we collect sensitive information directly from individuals seeking as-

sistance via internet search on issues of mortgage default and home foreclosure.2 Specifically,

we aggregate Google search queries for terms like “foreclosure help” and “government mort-

gage help” to compile a novel Mortgage Default Risk Index (henceforth, MDRI). While these

and related searches are derived from all households, a universe that includes both owners

and renters, the bulk of such searches likely emanate from property owners as they are most

1There are over 45 million home mortgages outstanding in the United States valued at nearly 10 trilliondollars. Mortgage statistics are from the US Census Bureau ACS Community Survey 2011 via the FactFinderwebsite and debt totals are from the Federal Reserve Statistical Release z.1. December 6, 2012, P. 9, TableD.3.

2Conti and Sobiesk (2007) find that users are forthcoming when using internet search engines.

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likely to be concerned with mortgage default.3 We infer that when a user seeks help via

a Google search query such as “mortgage foreclosure help,” she divulges her concern about

mortgage failure or foreclosure. This makes the MDRI unique compared to other proxies

for mortgage default risk – we simply let households speak and then capture the pertinent

information related to their concerns about mortgage default. Other housing information,

such as housing sentiment surveys by organizations such as the University of Michigan, only

ask households if now is a good or bad time to buy a home, but do not ask sensitive questions

about mortgage default.4 Moreover, proxies such as mortgage delinquencies or foreclosures

only reflect information tabulated from lenders and thus may miss important incremental

information pertaining to “at risk” households. Further, these indices are also only available

at substantial lags.

Our index is related to the theory and empirics of mortgage default. According to the the

“double trigger hypothesis”, only a substantial loss of income, via unemployment for example,

will assure mortgage default when a borrower has negative equity (Foote et al. (2008)).5 A

borrower who has positive equity and experiences an adverse income shock can simply sell the

house, pay off the mortgage, and reap the proceeds. Mortgage datasets, however, typically

do not observe contemporaneous job loss or other trigger events post-origination. As such,

prior empirical work has sought to predict mortgage default using proxies for the portion of

homeowners with negative equity or aggregate unemployment rates. While these variables

separately measure each side of the double trigger, they provide no information on their

intersection; further, those controls were either not available in the public domain during the

crisis or lacked predictive power.6 Our MDRI measure plausibly captures the intersection of

the mortgage default double trigger as it tabulates search queries from households considering

default who may be concerned about both negative equity and a loss of income.

As a proxy for the intersection of the double trigger, one would anticipate that elevated

3We would like to thank the editor for this point.4It is also not clear that respondents would answer such questions truthfully. Singer (2002) contends that

survey respondents are less likely to truthfully answer sensitive questions.5See also Bricker and Bucks (2016). More broadly, the household’s overall liquidity position will also

include other financial assets and available credit. A decline in available liquidity can therefore also triggermortgage default (Elul et al. (2010)). The borrowers with negative equity may also be at risk of default dueto other triggers of mobility, such as divorce. We would like to thank and editor for this latter point.

6Proxies of portion of homeowners with negative equity were only available from highly proprietary sourcesduring the crisis (see e.g. Haughwout and Okah (2009)) and only a small percentage homeowners with negativeequity default (Foote et al. (2008)). Aggregate unemployment rates lack predictive power and are often leftout of empirical models of mortgage default (Gyourko and Tracy (2014)).

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aggregate search for the terms that constitute the MDRI should translate into higher levels of

mortgage delinquency and default. From there, recent work on the foreclosure externalities

suggests that increases in the MDRI should work to dampen returns to housing (foreclo-

sure neighborhood spillovers). Indeed, Lambie-Hansen (2015) finds that seriously delinquent

homeowners neglect housing maintenance, subsequently depressing neighboring property val-

ues (Gerardi et al. (2015)) and leading to further increases in mortgage defaults (Chan et al.

(2013)). Anenberg and Kung (2014) also find that higher levels of foreclosures can increase

housing supply and thus depress property values.

Using the MDRI, we study the predictive effects of our internet-based mortgage default

risk measure on key housing indicators at the daily, weekly, and monthly frequencies.

At the daily level, we examine the predictive relationship between the MDRI and indices

that track the costs of subprime mortgage credit-default swaps (contracts that price the risk of

default on securitized subprime mortgage bonds traded between parties) via the Markit ABX

indices. Intuitively, the ABX represents an index of the cost of default insurance for subprime

mortgage backed securities. In this sense, the ABX indices are the financial market analog of

the MDRI for subprime borrowers: They reflect market participants’ expectations of the risk

of subprime mortgage default. Note also that the ABX indices are liquid measures traded

frequently by practitioners and institutional investors. Thus, by an efficient markets hypoth-

esis argument, if the MDRI predicts the ABX indices, it would suggest that the household

default risk captured by the MDRI was initially unknown to and later discovered by market

participants. Indeed, following an increase in the MDRI, financial market participants de-

mand larger premiums to insure subprime mortgage bonds against default. Thus, our index

appears to capture key incremental information regarding credit default risk and mortgage

market performance. In general, our results regarding the MDRI and subprime credit-default

swaps are noteworthy as the ABX indices acted as a leading indicator of the widespread

turmoil that permeated through financial markets during the recent crisis (Longstaff (2010)).

At the weekly and monthly frequencies, we assess the predictive effects of the MDRI

on a number of house price indices, measures of housing sentiment from household surveys,

and delinquency and foreclosure proxies. First, the weekly and monthly results indicate

that increases in the MDRI lead to a decrease in house price returns, in line with negative

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externalities due to delinquency or foreclosure. These results are consistent in- and out-of-

sample, across various house price indices including the FHFA, Case-Shiller, and the near

real time weekly house price index of Anenberg and Laufer (2014). Further, we find that

the MDRI is a leading indicator of housing market sentiment tabulated through the monthly

University of Michigan Consumer Sentiment Survey. Specifically, increases in the MDRI are

followed by an increase in survey responses that cite that now is a bad time to buy a home

because the future is uncertain or because the household cannot afford to buy a home. Hence,

the movements in the MDRI reveal mortgage distress before similar information is captured

by traditional survey measures. Finally, with regard to delinquencies and foreclosures, we find

that the MDRI predicts 60-day delinquencies, a common measure of default in the housing

industry.

Use of Google search data to develop a mortgage default risk index has a number of

distinct advantages. First, as noted above, our data allow us to directly observe the relative

frequency of Google internet search on foreclosure and related terms and thus to infer the

extent of mortgage default risk. Other survey-based sentiment indicators, such as those from

the University of Michigan, do not ask sensitive questions about mortgage default and it is not

clear that respondents would be forthcoming in responding to such questions.7 Second, the

Google search data aggregate queries from millions of households across the United States.

Accordingly, we have access to a substantially larger sample than the typical survey. Third,

Google data are available in real time (Choi and Varian (2012)), whereas traditional housing

indices are available only with some lag. Fourth, other proxies such as home foreclosures

only provide ex post information on mortgage default decisions, while indicators like the

VIX volatility index may reflect economic influences unrelated to housing markets. In sum,

our index is useful since it is broad-based, directly captures mortgage default risk, predicts

common housing and mortgage variables both in- and out-of-sample, and is available in real

time. Given those attributes, the MDRI provides new information and comprises a new and

useful tool for market participants, regulators, and policymakers seeking timely insights as to

the performance and outlook for housing and related sectors.

Although the MDRI is to the best of our knowledge the first application of Google data

to measure mortgage default risk, other papers have applied search query data in different

7Singer (2002) contends that survey respondents are less likely to truthfully answer sensitive questions.

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contexts. For example, Da, Engelberg, and Gao (2015) use Google data to develop a measure

of investor sentiment in equity markets, while Beracha and Wintoki (2013) use Google data

to proxy local housing demand.8 Our work is fundamentally different from previous studies

as we use search query data to develop a novel measure of mortgage default risk and show

that this measure predicts fluctuations in key housing and mortgage indicators.

The remainder of this paper is organized as follows: Section 2 describes the housing and

macroeconomic data; in section 3, we outline the internet search query used in this paper

and the construction of the MDRI; section 4 discusses the main results; the limitations of the

MDRI are outlined in section 5; and section 6 concludes.

2 Data

We employ data at the daily, weekly, and monthly frequencies to assess the predictive effects

of the MDRI in housing markets. Appendix C provides a complete list of the variables

used in this study along with descriptions, data abbreviations, the periodicities for which

each variable is used, and data transformations necessary to ensure stationarity. Below in

section 3, we provide a detailed description of the Google internet search query data and the

construction of the MDRI.

2.1 Daily Data

Our daily dataset includes the Markit ABX indices and the Chicago Mercantile Exchange

Case-Shiller (CME CS) futures. The ABX indices are key daily housing market indicators

and closely followed by institutional traders and other market participants. The ABX indices

track the cost of credit-default swaps on subprime mortgage-backed securities of a certain

investment grade and fall as the costs to insure mortgage-backed securities rise.9 Hence, the

ABX indices decline as investors become more bearish about housing and mortgage market

performance. Note that the ABX indices reflect the mortgage and housing market pessimism

of professional traders as only institutional investors participate in the trading of mortgage-

8For other recent applications of search query data, see Baker and Fradkin (2011); Bollen, Mao, andZeng (2011); Castle, Fawcett, and Hendry (2010); Ginsberg et al. (2009) Goel et al. (2010), Mondria, Wu,and Zhang (2010), Schmidt and Vosen (2011), Stephens-Davidowitz (2011), and Carriere-Swallow and Labbe(2011), Da, Engelberg, and Gao (2011), and Tkacz (2013).

9As suggested by the Wall Street Journal, the ABX indices “are some of the most closely watched [subprimemortgage] barometers on Wall Street.” The Wall Street Journal. June 21, 2007. “Index With Odd Name HasWall Street Glued; Morning ABX.HE Dose.” See also “Goldman Pushes Subprime ABX Index as HousingRebounds: Mortgages.” Bloomberg News, November 30, 2012.

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backed credit default swaps. The ABX indices are segregated by credit tranche and range

from AAA to BBB-. Separate ABX indices were issued in the first half of 2006, the second

half of 2006, the first half of 2007 and the second half of 2007. In our analysis below, we use

the “on the roll” log first-differenced ABX returns for each credit tranche. For further details

on the ABX indices, see appendix D.

To track house prices at the daily frequency, we employ the CME CS futures. Although

trading in the CME CS index is limited, these futures provide the most up-to-date, real-time,

proxy for house price expectations in financial markets.10 For our analysis below, we consider

the log first-difference (returns) of the CME CS futures. House price returns computed using

CS data do not account for maintenance, taxes, insurance, and the like.

Our daily dataset also includes a number of daily financial and economic indicators that

are used as controls. Specifically, we consider the Aruoba, Diebold, and Scotti (2012) ADS

index that tracks business conditions at the daily frequency; the default spread on corporate

bonds (BAA minus AAA corporate bond yields; CorpSpread); the VIX stock market fear

gauge; S&P500 stock returns (SPY); the Treasury Spread (10-year Treasury minus 2-year

Treasury; TreasSpread); and newspaper-based Economic Policy Uncertainty (Uncertainty)

from Baker, Bloom, and Davis (2013). The ADS captures the state of the business cycle,

CorpSpread is a proxy for credit risk, the VIX and SPY measure the dynamics of equity

markets; TreasSpread is an indicator for the yield curve; and Uncertainty measures policy

uncertainty and media sentiment.

2.2 Weekly Data

At the weekly frequency, house price data are from Anenberg and Laufer (AL; 2014).11 AL

use de-listings, rather than sales, to develop “a more timely house price index” that can be

computed in near real time. Indeed, AL compare their index to one that uses the typical

repeat sales (Case-Shiller (CS)) methodology and find that their de-listings index yields a

house price measure that signals house price movements much earlier than an index that uses

the CS methodology. Below, we consider both de-listings (AL) and repeat-sales (CS) log

10As the regular, monthly Case-Shiller house price indices are reported with a two month lag, a CME CSfutures contract with a settlement date in month t will be based on a the Case-Shiller house price index formmonth t− 2.

11We would like to thank Elliot Anenberg for providing us with this data.

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first-differenced house prices (returns) at the weekly frequency.12 This data ranges from June

2008 to October 2012. Other data at the weekly frequency include the aforementioned daily

controls aggregated to a weekly periodicity.

2.3 Monthly Data

Last, we consider a number of housing and economic proxies at the monthly frequency. The

housing data include housing sentiment from the University of Michigan, house price returns

from Case-Shiller and the FHFA, and delinquencies and foreclosures.

In their survey of consumer sentiment, the University of Michigan asks respondents if “now

is a good or bad time to buy a house and why.” The survey further provides participants

with an opportunity to explain why “now is a bad time to buy a house.” We focus on the

survey responses that cite that now is a bad time to buy a house because (1) the respondent

cannot afford a home (CantAfford); or (2) the future is uncertain (UncertainFuture).

At the monthly frequency, we test for predictive effects of the MDRI using quality-

adjusted, repeat sales house price indices from both Federal Housing Finance Agency (FHFA)

and Case-Shiller.13 Note that the Case-Shiller house prices are calculated using a three month

moving average, producing substantial serial correlation.14 This makes prediction with regard

to housing returns difficult after accounting for the autocorrelation. We employ the monthly

Case-Shiller and FHFA house price data at the national level and Case-Shiller data at the

metropolitan level. The local Case-Shiller data cover the largest metro housing markets.15

All house price data are transformed into log first-difference (return) form.

Our data also include first-differenced delinquency and foreclosure indices from Bloomberg

that cover 30-, 60-, and 90-day delinquencies and foreclosures for prime, subprime, and all

mortgages at the national level for the monthly periodicity. We transform the first-difference

of these indices to have have variance 1 over our sample period.

In line with the recent mortgage default literature (Aron and Muellbauer (2016)), our data

12As above, these computed house price returns do not include maintenance, taxes, or other related factors.13The FHFA and Case-Shiller data differ in a number of ways. First, the FHFA series is confined to sales

or re-finance of houses using conventional, conforming mortgages, whereas the Case-Shiller series includessales and re-finance of houses using all mortgage types, including subprime, Alt-A, jumbo, etc.

14Ghysels et al. (2012) find that the AR(1) coefficient for Case-Shiller national housing returns is 0.94;whereas that for FHFA national housing returns is 0.76. See also Case and Shiller (1989) for more about theserial correlation in housing returns.

15These housing markets include Atlanta, Boston, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit,Las Vegas, Los Angeles, Miami, Minneapolis, New York, Phoenix, Portland, San Diego, San Francisco, Seattle,Tampa Bay, and Washington DC.

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also includes indicators for mortgage default risk including the interest spread between 30-year

fixed rate mortgages and the 30-year Treasuries (MortgSpread), the log of the loan-to-price

ratio for new mortgages (Loan-to-Price)16, and the log first difference in the the number of

adjustable rate mortgage (ARM) applications (ArmApplications) as proxy for private mort-

gage credit demand. Other studies that aim to predict mortgage default risk also include

the household mortgage debt service ratio and proxies for the portion of mortgage borrowers

facing negative equity. Unfortunately, the debt service ratio is only available quarterly, while

negative equity proxies are only publicly available for post-crisis data.17 In unreported results,

we include the quarterly debt service as a control by filling in the monthly values with the

quarterly average; our below results are unchanged.

Finally, our monthly dataset also contains a number of controls that track housing markets

and the macroeconomy. These controls include the corporate default spread (CorpSpread);

the Housing Affordability Index (Afford); the civilian employment-population ratio (Employ-

ment); housing starts (HouseStarts); industrial production (INDPRO); retail sales (Retail-

Sales); the 10-year Treasury minus the 2-year Treasury (TreasSpread); and newspaper-based

economic policy uncertainty (Uncertainty).

3 Internet Search Query Data and the MDRI

We develop our Mortgage Default Risk Index (MDRI) using internet search query data from

Google Trends.18 Google is the most popular search engine in the United States. As of

December 2013, Google accounted for 66.5 percent of all US internet searches.19 Furthermore,

according to the Pew Research Center, 92 percent of online adults use search engines.20 Hence,

internet queries through Google are representative of the US internet population.

Google Trends reports the search frequency for a given search term relative to all other

search terms in the form of a Search Volume Index (SVI). Appendix E provides a detailed

overview of the Google Trends SVIs.

16Previous literature uses loan to value (LTV), but LTV is only available at the quarterly periodicity forUS data. Thus, we use the Loan-to-Price ratio.

17The debt service ratio is from the Federal Reserve Board, while estimates for the number of homeownerswith negative are publicly available from Zillow beginning in 2011 or can be tabulated from proprietary CoreLogic Data. For an example of the latter see Bhutta, Dokko, and Shan (2010).

18https://www.google.com/trends/19As measured by Experian, a company that monitors internet trends.20See http://www.pewinternet.org/Reports/2011/Search-and-email/Report.aspx.

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To construct the MDRI, we begin by considering housing and mortgage related keywords,

such as “mortgage” and “foreclosure,” in combination with the word “help.” The term “help”

is the most commonly queried mortgage default signal according to Google Adwords.21

Combining the word “help” with terms “mortgage” and “foreclosure” yields two obvious

Google queries that can be immediately used as a starting point: “mortgage help” and “fore-

closure help.” Entering “foreclosure help” and “mortgage help” into Google Trends produces

a report that contains similar queries. We compile our list of search terms from those high-

lighted by Google Trends on the condition that they contain a housing or mortgage keyword

and an indicator of distress. This process leads to 11 key mortgage default and related search

query terms.22 All of the search terms contain a housing keyword along with the word “help”

or “assistance.”

In general, the search keywords used in the construction of the MDRI were queried fre-

quently by internet users. According to Google Adwords, during 2012, well after the housing

crisis peaked, the phrases “foreclosure help” and “mortgage help,” two search keywords used

in the construction of our index, were queried 266,400 and 594,000 times, respectively. This

suggests that our mortgage default risk index is the compilation of millions of internet search

queries since 2004 and captures an extensively larger sample than the typical consumer sur-

vey.23

Figures 1 and 2 show the screenshots from Google Trends for the SVIs for “mortgage

assistance” and “government mortgage help” at the monthly periodicity. As seen in the

figure, searches for the term “mortgage assistance” rise well before the start of the bear

market in 2007M10 and peak just prior to the end of the bear market in 2009M03. On

the other hand, searches for the term “government mortgage help” are almost nonexistent

during the housing bubble, rise as the bear market begins, and then skyrocket at the peak of

the housing crisis. Together, these figures document the timely nature of the mortgage and

21Over the 12 months prior to November 2012, U.S. internet users searched for the term “help” approxi-mately 20.4 million times per month while queries for related terms such as “relief,” “assistance,” and “aid”totaled just 2.7 million, 5.0 million, and 9.1 million per month, respectively. Google Adwords reports thenumber of queries over a 12 month moving average for each search term. We do not consider the word“support” as this term is often queried in association with “child support” in a housing related context.

22These search terms include foreclosure assistance; foreclosure help; government assistance mortgage;home mortgage assistance; home mortgage help; housing assistance; mortgage assistance program; mortgageassistance; mortgage foreclosure help; mortgage foreclosure; mortgage help.

23The University of Michigan Survey Research Center, for example, surveys just 500 households per monthto produce their Consumer Sentiment Index.

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housing search queries and show how they aptly signal mortgage default risk.

We use Google Trends data at the daily and monthly frequencies. Note that there are dif-

ferences in how Google Trends reports the data at the different frequencies and we must alter

our approach for each periodicity to accommodate these differences. Appendix E provides a

detailed overview of our approach.

At the daily frequency, the levels MDRI is the percentage growth in the MDRI from the

first period (March 3, 2004). Also, to ensure that we are not constrained by Google Trends’

privacy filter, we add the terms “foreclosure” and “foreclosures” to our set of search keywords

used to construct the daily index.24 Our list of search terms used at the daily frequency are

in the top panel of table 1.

To build the monthly MDRI, we query Google Trends for the joint SVI for the 11 search

terms in the bottom panel of table 1.25

Last, we do not apply any seasonal adjustment to the MDRI to retain the real-time nature

of the index and avoid any “look-ahead” bias.26 In a previous version of the paper, we season-

ally adjusted the MDRI. The results are similar, indicating that the results presented below

are robust to seasonal adjustment. More detail about the MDRI at different periodicities in

described in sections 3.1, 3.2, 3.3, and appendix E.

3.1 The Daily MDRI

We plot the levels daily MDRI versus other key daily housing market and economic indicators

in figure 3. The shaded bar in the plot represents the bear market over the sample period.27

As seen in the top panel of the figure, the MDRI stays low in 2004 and 2005, and then rises

substantially between 2006 and 2008, highlighting the increase in the mortgage default risk

over the sample period.28 Then in 2009, the MDRI starts to fall as the housing and mortgage

24In unreported results, we also build the monthly MDRI using the search terms listed in the top panel oftable 1. The correlation between our monthly MDRIs that use these different search terms is 0.97.

25Specifically, the joint SVI is obtained from Google Trends by entering the search terms separated bya plus sign (“+”). To get the monthly SVI for the MDRI we enter the following into Google Trends:“foreclosure assistance+foreclosure help+government assistance mortgage+home mortgage assistance+homemortgage help+housing assistance+mortgage assistance program+mortgage assistance+mortgage foreclosurehelp+mortgage foreclosure+mortgage help”

26We would like to thank and anonymous referee for this point.27Bear markets are defined as a 20 percent or more drop in the S&P500 over a period of two or more

months.28Note also that Permits and housing starts also peaked in late 2005, when the MDRI was lowest. Thus,

home builders may have anticipated the housing dynamics reflected in the MDRI. See Liu et al. (2014) andDeCoster and Strange (2012).

10

crisis begins to abate.

The following three panels show the plots for the ABX AAA, ABX A, and ABX BBB

indices. Note that the ABX indices start at 100 on the day of issuance and fall as investors

become more bearish on housing and mortgage market performance. While signs of distress

surfaced in the MDRI in mid-2006, the highly-rated ABX AAA index did not begin to fall

until the start of the bear market in October 2007. From there, the ABX AAA index plunged

during the crisis period and then, like the MDRI, recovered in the aftermath of the crisis.

The following two panels plot the path of the ABX A and ABX BBB indices. These lower-

rated indices also dropped dramatically during the bear market, but never recovered in the

aftermath of the crisis.

The next panel shows the path of the CME Case-Shiller futures. Although trading in the

CME CS house price futures is limited they closely track the dynamics of the housing market.

Indeed, the bottom panel in figure 3 shows that the CME CS futures plummeted during the

crisis, waffled at the lower end of their range following the crisis and then recovered in the

aftermath of the crisis.

Finally, the bottom two panels plot the paths of the ADS business conditions index and

the corporate default spread (CorpSpread). These key economic indicators did not reveal

signs of crisis until the beginning of 2008, well after the rise in the MDRI.

The correlations between the daily MDRI and the other housing and financial indicators

are displayed in table 2. First, we show the correlation between the MDRI and the VIX index.

The VIX index is commonly referred to as a “fear index” for the stock market. So, the large

and highly significant correlation coefficient between the daily MDRI and the daily VIX index

shown in the table indicates that distress in equity markets was closely related to default risk

in housing markets over the sample period. The following rows show the correlations between

the daily MDRI and housing indices available at the daily frequency. The results indicate

that the MDRI is negatively related to the returns on the the CME Case-Shiller (CME CS)

futures and ABX indices. These correlation coefficients are all significant at the 10 percent

level.

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3.2 The Weekly MDRI

We construct our MDRI index at the weekly periodicity for use in the prediction of the near

real-time housing price indices of Anenberg and Laufer (2014). To build our weekly MDRI,

we average the daily MDRI over each week. The weekly house price data are available from

June 2008 to October 2012.

3.3 The Monthly MDRIs

Last, the plots for the national MDRI and other national housing indices at the monthly

frequency are displayed in figure 4. The top panel of the figure shows the path for the

monthly MDRI standardized to have zero mean and unit variance. The MDRI begins to rise

in 2006, accelerates upwards in 2007 and 2008, peaks at over 4 monthly standard deviations

at the apex of the crisis in early 2009 and then falls in the aftermath of the crisis. Overall,

the MDRI appears to closely track mortgage default risk during our sample period.

The plot in the second panel shows the Case-Shiller house price returns. Clearly, these

returns document the evolution of the recent crisis: They begin to fall starting in 2006, plunge

in 2007, bottom out at around −2 percent per month in 2008, waffle at around zero percent

per month between June 2009 and the end of 2011, and become positive again thereafter.

Furthermore, comparing the paths of the MDRI and the Case-Shiller returns suggests that

mortgage default risk and housing market returns are highly inversely related. Next, in the

third panel of the figure, we plot a University of Michigan survey-based measure of housing

market sentiment labelled UncertainFuture. The plot indicates that UncertainFuture did not

begin to rise until 2008, well after the start of housing crisis, and remained high after the

crisis until 2013. In contrast, the MDRI began to rise in 2006 and fell beginning in 2009 as

the effects of the crisis abated. Together, these plots suggest that the MDRI acts as a leading

indicator of the survey-based UncertainFuture and thus that mortgage default risk measures

a different dimension of household behavior compared to that captured by UncertainFuture.

The final two plots show the path of 30-day delinquencies and foreclosures over our sample

period. 30-day delinquencies begin to rise in 2006, jump in 2007 and 2008, and peak in 2009.

Hence, the path of delinquencies is similar to that of the MDRI during the lead up to the

crisis. Yet unlike the MDRI, 30-day delinquencies only fall slightly in the aftermath of the

crisis. This latter result suggests that delinquencies remained elevated in the aftermath of

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the crisis even as the MDRI began to decline. Finally, the bottom panel of figure 4 shows

the plot of foreclosures. Foreclosures began to rise in late 2007, shot up between 2008 and

2010, and remained elevated in the wake of the crisis. Comparing the plots of the MDRI

and foreclosures suggests that our mortgage default risk proxy acts as a leading indicator of

foreclosures over the sample period.29

Table 3 displays the correlations between the monthly MDRI and the monthly housing

indicators. The results indicate that the MDRI is closely related to housing sentiment mea-

sures: The correlation between the MDRI and CantAfford is 0.63, while that between the

MDRI and UncertainFuture is 0.68. Thus, the MDRI is related to consumers’ concerns about

home purchase affordability and future housing and economic uncertainty. The second panel

in table 3 shows the contemporaneous relationship between the MDRI and the monthly US

housing returns reported by Case-Shiller and the FHFA. In line with our expectations, the

MDRI is highly inversely correlated with housing returns. The following three panels show

that the MDRI is correlated with all of the delinquency proxies, but most closely related

to instances of delinquencies at 60 days. Indeed, the correlations between the MDRI and

the 60-day delinquency measures range from 0.72 to 0.86. These results correspond with

our expectations as mortgage lenders and loan servicers typically only seek to work out loan

delinquency or initiate foreclosure proceedings after 60 days. Further, delinquency beyond

60 days is considered default in much of the housing literature. See An, Deng, and Gabriel

(2016) for more details.

The bottom panel in table 3 presents the correlations between the MDRI and the foreclo-

sures. Although the MDRI is correlated with foreclosures, the coefficients are smaller than

those found for house price returns or delinquencies. Thus, households appear less likely to

signal their risk of default at the foreclosure stage.

Finally, we construct the MDRIs at the local level for the markets where the Case-Shiller

house price indices are available. Our methodology is identical to that described above.

However, to help circumvent Google Trends’ privacy filter and assure adequate local sample

size, we also add the words “foreclosure” and “foreclosures” to our list of search terms. We

29As noted by a referee, government programs to limit foreclosures over the sample period may havepropped up house prices and pushed foreclosures further into the future. This may explain the elevated levelsof delinquencies and foreclosures after the crisis and low levels of housing returns even after the recessionended and the financial crisis abated.

13

retain the local MDRIs for housing markets where search volume exceeds Google’s privacy

filter in all time periods. This process yields 16 local MDRIs.30 We plot the local MDRIs

for Dallas, Denver, Miami, and Phoenix in figure 5. Specifically, in the plot we show the

percentage increase in the local MDRIs from 2006, the peak of the housing boom, to the end

of the sample period. Clearly, there is substantial variation in the path of the local MDRIs.

For example, there is relatively little movement in the MDRIs for Dallas and Denver, cities

that did not experience a major housing boom and bust over our sample period. In contrast,

the MDRIs for Miami and Phoenix rise spectacularly during the housing bust beginning in

2007 and then fall markedly in 2011 as the housing market started to recover.

4 Main Results

In this section, we assess the predictive effects of the MDRI at the national level, examine the

robustness of those results, and study the predictive power of the MDRI across local housing

markets. Further, section 4.2 measures the economic impact of increases in the MDRI on key

variables of interest using vector autoregressions; we examine if there is any feedback between

the housing market proxies and the MDRI in section 4.4; and section 4.5 assesses the out-of-

sample forecast performance of the MDRI. Note that we consider the MDRI minus its 1-year

(one-sided) moving average (MDRI ma) in all of our predictive models.31 Using the MDRI

minus its 1-year moving average allows us to capture the longer term trends in the MDRI

while at the same time ensuring stationarity over all sample periods.32 Note that MDRI ma

is standardized to have zero mean and unit variance. Further, the ABX indices, CME CS

futures, and house prices are all in log first-differenced (return) form, and the foreclosure and

delinquency measures are in first differences.

4.1 National-Level Results

We start by analyzing the predictive effects of the national MDRI on national housing market

data at the daily, weekly, and monthly periodicities. All lag lengths are chosen by the Akaike

Information Criterion (AIC). The robustness of the lag length selection criterion and choice

30The local MDRIs are available for Atlanta, Boston, Chicago, Dallas, Denver, Detroit, Los Angeles,Miami, Minneapolis, New York, Phoenix, San Diego, San Francisco, Seattle, Tampa Bay, and WashingtonDC.

31At the daily frequency, we use the MDRI minus its 250 day (one-sided) moving average. At the monthlyfrequency, the key measure of interest is the MDRI minus its 12-month moving average.

32We would like to thank an anonymous referee for pointing us in this direction.

14

of control variables are assessed in section 4.3.

4.1.1 The MDRI and Daily Housing Indices

The daily housing proxies considered in this section, such as the ABX indices, are among the

most up-to-date, real time indicators of the housing and mortgage market performance. Thus,

predictive effects of the MDRI on these daily housing market measures would imply that the

MDRI is leading indicator of the US housing market. The results are shown in table 4. In the

top panel, we show the estimated coefficients on lags of MDRI ma; White heteroskedasticity

robust standard errors are in parentheses. The middle panel shows the number of lags chosen

for the dependent variable and the exogenous regressors. The control variables include key

economic and financial indicators including the ADS business conditions index, the corporate

default spread (CorpSpread), the first-difference of the VIX index (∆VIX), S&P500 (SPY)

stock returns, the 10-year minus 2-year Treasury spread (TreasSpread), and the newspaper-

based Uncertainty index. We include the Uncertainty index as the tone of news reports may

affect agents’ internet search behavior. The middle panel displays the F-statistic correspond-

ing to the Granger Causality test where the null hypothesis is that MDRI mat−1 = · · · =

MDRI mat−j = 0. The F-statistic is computed using the White heteroskedasticity robust

variance-covariance matrix.

The first five columns show the predictive power of the MDRI with respect to the ABX

indices that track the cost to insure subprime mortgage backed debt. These results indicate

that the MDRI is in fact a leading indicator of the ABX indices: A one standard deviation

increase in MDRI mat−1 predicts, for example, a decrease in the daily ABX AAA returns of

0.11 percentage points. This effect is statistically significant at the 1 percent level. Similarly,

when any of the other ABX indices represent the dependent variable, the Granger Causality

F-statistics are all large in magnitude and significant at the 5 percent level. Thus, the MDRI

appears to be useful in predicting the ABX returns. Last, columns (6) and (7) show the

regressions where the returns on the Case-Shiller (CS) CME Futures or ∆VIX represent the

dependent variable. In these cases, we find that the MDRI has no predictive power; yet there

is limited trading in the CS CME futures and the VIX index is more related to expected risk

in the stock market.

In total, the findings in this section imply that the MDRI is a leading indicator of the most

15

up to date, real time measures of housing and mortgage performance. Thus, the MDRI pro-

vides practitioners and policymakers with a key real time tool to assess the current direction

of household mortgage performance.

4.1.2 The MDRI and Weekly Housing Indices

Next, we examine the predictive power of the MDRI with regard to weekly house price

returns computed using the repeat-sales (Case-Shiller; CS) and de-listings (Anenberg-Laufer;

AL) methodologies. The results are in table 5. The format of table 5 is identical to that used

above in table 4 and the controls used in table 5 are the daily variables aggregated to the

weekly frequency.

The results from table 5 indicate that the MDRI predicts weekly house price returns using

both the repeat-sales (CS) and de-listings (AL) indices. First, for the repeat-sales CS returns

in column (1), higher levels of default risk, as measured by the MDRI, predict lower future

house price returns. Yet for the AL house price returns in column (2), the predictive effects

are only significant at longer lags. This implies that housing information transmits more

slowly through the AL index. Finally, as indicated by the F-statistic in the third panel, we

reject at the 10 percent level the Granger Causality null that the MDRI has no predictive

power for house price returns when either the CS or AL house price returns are the dependent

variable.

4.1.3 The MDRI and Monthly Housing Indices

In tables 6 and 7, we assess the predictive power of the MDRI at the monthly frequency.

The format of these tables is identical to those discussed above. Model controls include key

housing market indicators such as the housing affordability index, standard mortgage default

proxies, housing starts, and 30- and 90-day delinquencies. We also control for a number

of economic indicators including the civilian employment-population ratio, the growth in

industrial production, and economic uncertainty.

First, columns (1) and (2) of table 6 show the predictive relationship between the MDRI

and housing sentiment from the University of Michigan Consumer Sentiment Survey. The

results indicate that the an increase in the MDRI, relative to its 1-year moving average,

leads to an initial increase in the first-difference in UncertainFuture and the first-difference

of CantAfford. While different individual lags of MDRI ma are statistically significant in

16

both models, only the Granger Causality F-statistic in column (1) where ∆CantAfford is the

outcome variable is significant at the 10 percent level. Next, columns (3) through (6) show

the predictive effects of the MDRI on the Case-Shiller and FHFA housing returns. In columns

(4) and (6), we interact MDRI ma with a crisis indicator that takes a value of 1 during the

crisis and zero otherwise (2007M01 - 2009M03).33 For the full sample, increases in MDRI ma

predict lower house price returns. A one standard deviation increase in MDRI ma leads to a

decrease in the Case-Shiller house price returns of 0.08 percentage points after two months.

Note that although MDRI mat−1 and MDRI mat−2 are not statically significant, we do reject

the null that these two coefficients are jointly equal to zero at the 10 percent level. This effect

then reverses in the third month. Similarly, when house price returns are calculated using

the FHFA index, increases in MDRI ma lead to a decrease in returns the following month.

These predictive effects are summarized by the MDRI ma Granger Causality F-statistic that

is significant at the 5 percent level when either the Case-Shiller or FHFA returns represent the

dependent variable. When we interact the MDRI with the crisis indicator in columns (4) and

(6), the results show that increases in MDRI ma predict even lower housing returns during the

crisis for both the Case-Shiller and FHFA indices. Indeed, as seen in the second to the bottom

panel of the table, the null that tests that MDRI mat−1*crisis= · · · = MDRI mat−j*crisis = 0

is rejected at the 10 percent level for both the Case-Shiller and FHFA returns, providing

statistical evidence that predictive effects of MDRI ma are stronger during the crisis period.

Section 4.2 provides more detail regarding the magnitude of these effects and their economic

significance.

Next, table 7 shows the predictive power of the MDRI with regard to delinquencies and

foreclosures. Specifically, we consider 30-, 60-, and 90-day delinquencies and foreclosures for

all loan types, prime loans and subprime loans. The control set is identical to that used in

table 6. Overall, the results indicate that the predictive effects of the MDRI relative to its 1-

year moving average are largest for 60-day delinquencies, 90-day delinquencies, and prime and

subprime foreclosures: For example, a one standard deviation increase in the MDRI ma leads

to 0.21 standard deviation increase in 60-day Delinquencies and a 0.24 standard deviation

increase in 60-day Subprime Delinquencies. Yet we also find that the MDRI predicts 90-day

delinquencies for all loans and subprime loans as well as for prime and subprime foreclosures as

33In unreported results, we considered several other dates for the start of the crisis, the results are similar.

17

suggested by the large and highly significant Granger Causality F-statistics. Thus, our results

indicate that the MDRI is a key predictor of later stage delinquencies and foreclosures.

In total, the results in this section imply that the MDRI acts as leading indicator of housing

sentiment, house price returns, and 60-day delinquencies. Further, for housing returns, we

find that the predictive effects of the MDRI are stronger during times of crisis. Together,

these findings suggest that that the MDRI captures household mortgage default risk and

yields a more timely indicator of housing performance than previously considered housing

market proxies.

4.2 Vector Autoregressions

To measure the economic magnitude of the predictive effects outlined in the previous section,

we compute reduced form impulse responses within a vector autoregression (VAR) framework

at both the daily and monthly frequencies. The daily VAR includes the following variables:

MDRI ma, ABX BBB- returns, ABX BBB returns, ABX A returns, ABX AA returns, ABX

AAA returns, and S&P500 returns. At the monthly frequency, we similarly use the fol-

lowing variables: MDRI ma; Case-Shiller and FHFA returns; the first-difference in 30-Day

Delinquencies, 60-Day Delinquencies, 90-Day Delinquencies, Foreclosures; and the growth

in Industrial Production.34 At both the daily and monthly levels, VAR lags are selected

using the AIC and we trace out dynamic responses following an innovation that increases

MDRI ma by 2 standard deviations. Increases in MDRI ma of 2 standard deviations or more

are common in our sample and occur 65 times in the daily data. 90 percent bootstrapped

confidence intervals for the impulse response functions (IRFs) are computed using a two-step

bias corrected stationary block bootstrap procedure as in Politis and Romano (1994) and

Kilian (1998).35 Note that we also apply the Kilian bias correction to the point estimates and

that 1000 repetitions are used in the bootstrap procedure. At the daily frequency, we use a

block size of 10 days and trace out the IRFs for 15 days. For the monthly data, the block

size is 6 months and we trace out the IRFs for 24 months.

The results are in figures 6 and 7 and tables 8 and 9. The daily IRFs in figure 6 show that

a 2 standard deviation innovation in MDRI ma leads a reduction in the returns across the

34Computing structural impulse responses using a Cholesky Decomposition with the variables in the afore-mentioned order produces similar results.

35For other implementations of the bootstrap procedure see Wright (2012) Gabriel and Lutz (2014).

18

ABX indices. Further, for all of the ABX indices, the upper confidence bound falls below the

zero line at some point and thus these IRFs are non-zero and MDRI ma Granger Causes the

set of ABX indices within this VAR framework.36 We document the economic significance of

dynamic effects in table 8 and show the cumulative VAR IRFs up to 15 days. The results are

telling: After 15 days, a 2 standard deviation innovation in MDRI ma leads to a 2.41 percent

drop in the ABX BBB-, a 2.58 percent drop in the ABX BBB index, and a drop of at least

1.25 in the other ABX indices. For the ABX BBB- index, the 2.41 percent drop corresponds

to an increase in the cost to insure $10,000,000 million of BBB- subprime mortgage backed

debt of $241,000 dollars.37

The monthly reduced form IRFs following a two standard deviation innovation that in-

creases MDRI ma are the black-solid lines plotted in figure 7; the corresponding 90 percent

boostrapped confidence intervals are the black-dashed lines. These IRFs indicate that hous-

ing returns fall for about three to six months following an an innovation in MDRI ma, while

30-, 60-, and 90-day delinquencies and foreclosures rise. The confidence intervals show that

the decrease in housing returns and the increases in 30- and 60-day delinquencies and fore-

closures are significant at the 90 percent level. Thus we reject the null MDRI ma does not

Granger Causes this set variables. The cumulative IRFs in table 9 show that the effects that

follow an increase in MDRI ma are large in magnitude: After 10 months, Case-Shiller prices

fall by 1.89 percent, FHFA prices fall by 0.61 percent and 60-day delinquencies rise by 1.96

monthly standard deviations. This latter result implies that a 2 standard deviation increase

in the MDRI ma leads to an increase in the percentage of loans that are 60-day delinquent

of 0.14 percentage points. For further comparison, the red-dotted lines in figure 7 show the

reduced form IRFs following an innovation to the Case-Shiller returns of −2 percentage points

(equivalent to approximately 2 standard deviations).38 Based on the 90 percent bootstrapped

confidence intervals (not shown), the reduced form IRFs following a 2 percentage point drop

in Case-Shiller returns are only significant for FHFA housing returns. Hence, a drop in Case-

Shiller returns does not necessarily predict a rise in delinquencies or foreclosures. Moreover,

following this decline in house prices, the IRFs for delinquencies initially move in the wrong

36Lutkepohl (2005); p. 54.37This is assuming that the “factor,” the portion on mortgages in the pool still outstanding, is equal to 1.

See appendix D for more details.38The monthly standard deviation of the Case-Shiller returns over our sample period is 0.97 percent.

19

the direction and are small in magnitude. Together, these results suggest that the MDRI

captures the intersection of the income losses and negative equity that constitute the double

trigger hypothesis of mortgage default, while housing returns alone have less predictive power.

4.3 Robustness

In this section, we assess the robustness of the foregoing results. Specifically, we consider

various permutations of the variables in the control set of our predictive regressions and an

alternative lag length selection criterion, the Bayesian Information Criterion (BIC). The full

results are in an online appendix. This approach allows us to consider an extensive number

of permutations of the control set. We conduct these robustness checks for all of the key

dependent variables of interest at the daily, weekly, and monthly frequencies.

We summarize these robustness checks in table 10. Here, we average the F-statistics

across all considered robustness checks and also report the bootstrapped standard errors

corresponding to the average F-statistics. In general, the average F-statistics are similar to

those found above and thus suggest that our findings are robust to different permutations of

the control set and different lag length selection criterion. Further, the bootstrapped standard

errors for the average of the F-statistics are generally small in magnitude, implying that there

is little variation in the results across model specifications. Overall, the findings from this

section indicate that our results are robust to the choice of various control variables and an

alternate lag length selection criterion.

4.4 Reverse Causality

In addition to examining the predictive effects of the MDRI on key housing indicators, we also

consider causality in the other direction: Do available housing market indicators predict the

MDRI? Specifically, we build predictive models where MDRI ma is the dependent variable

and the housing market proxies described above represent the key independent variables.

Table 11 presents the results with and without exogenous controls. At the daily, weekly, and

monthly frequencies, the controls are identical to those used above.

For the daily data at the 5 percent level of significance, we find that only the higher

rated ABX AAA index returns predict MDRI ma when control variables are included. At the

weekly level, the results indicate that house price returns based on the CS methodology are

useful in predicting the MDRI when no controls are included, but only at the 10 percent level

20

of significance. Similarly, in the bottom panel, results using monthly data imply that the

Case-Shiller and FHFA returns are also useful in predicting the MDRI. This finding is in line

with our expectations and suggests that changes in housing market performance re-enforce

the default risk captured by the MDRI.

The F-statistics in table 11 are significant at the 5 percent level with and without controls

when the 60-day delinquencies serve as the outcome variable. This result is also not surprising

as mortgage lenders and servicers usually send notices to homeowners regarding delinquency

after 60 days. Thus, upon receiving these notices, we would expect households to search for

information about delinquency or default. This further validates the MDRI as a measure of

mortgage default risk. Last, we also find that total and prime foreclosures have predictive

power with regard to MDRI ma.

4.5 Out-Of-Sample Forecasts

In the previous sections, we found that the MDRI was a leading indicator of several important

housing proxies. Here, we assess the ability of the MDRI to forecast the housing variables

of interest out-of-sample (OOS), one-step ahead at the daily and weekly frequencies. Our

benchmark model is an autoregressive (AR) model and the forecast model of interest is a

linear model with autoregressive lags and lags of the MDRI:

rt = α +

p∑i=0

βirt−p +

q∑j=1

γq ·MDRI mat−q (1)

where rt is the outcome variable of interest and β0 = 0, meaning that the selection criterion

can choose zero lags of the dependent variable (a random-walk plus drift model). Lags are

chosen based on the AIC. Using the BIC for lag length selection produces similar results as

shown in an online appendix. Forecasting models are estimated using a number of rolling

training windows. Shorter training windows allow the forecasting model to more quickly

adjust to changing dynamics in the variables of interest. This may be important given the

volatility of financial markets and the macroeconomy over our sample period. Longer windows

use more information and may yield more precise parameter estimates.

We assess forecast performance relative to the benchmark AR model using the Diebold-

Mariano (2002) test (null hypothesis is equal forecast accuracy between the model and the

benchmark) and the OOS R2 as in Campbell and Thompson (2007) and Goyal and Welch

(2008). The lags for the benchmark AR model are chosen using the AIC. The formula for the

21

OOS R2 statistic is

OOS R2 = 1− MSEmodel

MSEbenchmark

(2)

where MSEmodel and MSEbenchmark are the mean-squared forecast errors for the model of

interest and the benchmark AR model, respectively. Positive OOS R2 values indicate that

the model of interest outperformed the benchmark in terms of mean-squared prediction error.

We show the daily results for the ABX returns across the various training windows (in

days) in table 12. Columns 2 and 3 of the table display the first and last forecast date for

each training window. The remaining columns hold the OOS R2 statistics (in percentages)

for the returns on each of the ABX indices. The p-value from the Diebold-Mariano statistic is

in parentheses and one, two, and three asterisks indicate Diebold-Mariano p-values that are

less than 0.1, 0.05, and 0.01, respectively. As seen in the table, all of the OOS R2 statistics

are positive and large in magnitude, especially compared to the values found in the equity

prediction literature.39 Further, we reject the Diebold-Mariano null of equal forecast accuracy

at the 5 percent level whenever the ABX BBB or ABX BBB- returns are the outcome variable.

As the lower rated ABX indices were key leading indicators of other housing and financial

variables during the crisis (Longstaff (2011)), the foregoing results imply that households

signalled their default risk via the MDRI before this information was discovered by market

participants. The predictive effects are also particularly strong for training windows of 30, 40,

60, or 80 days. The notable performance of the forecasting model at these shorter training

lengths suggests that allowing the model parameters to change quickly improves OOS forecast

accuracy. This result is not surprising given the volatility of housing markets during the

sample period. In an online appendix, we show that our findings are similar when the BIC is

used for lag length selection or when the forecast window ends on December 31, 2009, around

the conclusion of the crisis.

Table 13 presents the weekly OOS results for house prices constructed using the both the

CS and AL methodologies. Note that these weekly HPIs run from June 2008 to October 2012.

Overall, all of the OOS R2 statistics are large in magnitude and the Diebold-Mariano p-value

is less than 0.01 when house prices are constructed using the repeat-sales CS methodology.

Hence, MDRI ma appears to be a leading predictor of house price returns out-of-sample.

39See Campbell and Thompson (2007), Goyal and Welch (2008), and Lutz (2015).

22

4.6 Mortgage Default Risk and the Cross-Section of Housing Returns

In this section, we examine the predictive effects of the local MDRIs on metropolitan housing

markets using monthly data. Specifically, we employ the following model:

rit = β1MDRI mai,t−1 + β2Controls + vit (3)

where rit is the local house price return for city i, MDRI mai,t−1 is the MDRI minus its 1-year

moving average for each city i and Controls is a vector of controls. The controls include

three lags of the dependent variable to account for the autocorrelation in house price returns,

one lag of housing starts to further capture local housing market dynamics, and one lag of

the local unemployment rate to measure changes in the local macroeconomy. We estimate

the model using both OLS and GMM. For the OLS estimates, we include month and city

fixed effects and compute the Driscoll and Kraay (1998) standard errors that accommodate

both cross-sectional and serial correlation. The GMM estimator is the “twoways” estimator

of Arellano and Bond (1991) that allows for both individual and time fixed effects. Standard

errors for the GMM estimates are calculated using the robust covariance matrix proposed

by Windmeijer (2005). For these local models, we use the monthly house price data from

Case-Shiller.

The results are presented in table 14. The left panel holds the OLS estimates; in the

right panel we show the GMM estimates. Columns (1) and (3) estimate the model outlined

in equation 3. In columns (2) and (4), we interact MDRI mai,t−1 with a crisis indicator that

takes a value of 1 between 2007M01 and 2009M03 and 0 otherwise. Thus, through columns

(2) and (4), we allow the predictive effects of the MDRI to vary across crisis and non-crisis

periods. First, as seen in the left panel via the OLS estimates, increases in the local MDRI

predict a decrease in local housing returns. Indeed, the estimates in column (1) imply that

a one standard deviation increase in the local MDRI relative to its 1-year moving average

predicts a decrease in metropolitan level housing returns of 0.04 percentage points. Column

(2) indicates that predictive relationship between the MDRI and house price returns is largely

concentrated during the crisis period: In the crisis period, a one standard deviation increase

in MDRI mai leads to a decrease in housing returns of 0.07 percentage points. Using OLS,

however, the coefficient estimates are not statistically significant.

Next, column (3) shows the GMM estimate for the model in equation 3. The results

23

suggest that a one standard deviation increase in MDRI mai leads to a 0.09 percentage point

decrease in local housing returns, an estimate that is significant at the 5 percent level. Last,

column (4) shows the GMM estimates when we allow the predictive impact of the MDRI to

vary across the crisis and non-crisis periods. Here, the predictive effects of the local MDRIs

are stronger during the crisis period.

5 Limitations of the MDRI

While the MDRI does provide policymakers and practitioners with a novel measure of mort-

gage default risk, there are some limitations to its use and applications. First, the MDRI

captures mortgage default risk at the household level and thus does not yield direct informa-

tion on the health of mortgage lenders or financial institutions. Hence, high levels of default

risk revealed through the MDRI may not necessarily signal a financial crisis or an economic

downturn if mortgage lenders and financial firms are sufficiently well positioned to handle

the corresponding elevated levels of household defaults. Second, the MDRI is compiled from

internet search queries via Google Trends. As such, the MDRI may be vulnerable to changes

in internet search behavior. For example, if the Google search engine falls out of favor with

consumers or new products (e.g. voice search or searches from mobile devices) alter search

patterns, the usefulness of the MDRI may be adversely affected. At this point, we have no

reason to believe that recent technological advances are changing internet search behavior,

but the risk remains. Further, related to the previous point, households appear to be willing

to divulge sensitive information in the context of internet searches.40 Going forward, internet

users may alter their search patterns as they become more aware of data retention policies

of various internet entities. This may be relevant for the MDRI as searches for mortgage

default keywords are sensitive in nature. Third, the MDRI only reveals default risk known

to households. Thus, if a negative shock occurs in a different sector of the economy and then

subsequently transmits to the housing sector, the MDRI may act as a lagging indicator during

the corresponding downturn. Last, the MDRI is, by its construction, a default risk indicator

and hence may be of limited use in the identification of a house price boom.

40See, for example, Conti and Sobiesk (2007).

24

6 Conclusion

In this paper, we apply internet search query data to develop and test a broad-based, real-time

gauge of default risk in mortgage markets. To do so, we first aggregate Google search queries

for terms such as “mortgage assistance” and “foreclosure help” to comprise a novel Mortgage

Default Risk Index (MDRI). We then assess the predictive properties of the new index and

its relationship to other housing and financial variables including the ABX indices that track

the cost of subprime mortgage credit-default swaps, survey based housing sentiment, house

price returns, and delinquencies and foreclosures. Unlike these more common indicators, the

MDRI directly reflects default risk revealed through search queries. This makes our timely

index unique as the MDRI captures a dimension of agent behavior not previously observed

in the literature.

We use the MDRI to further examine the predictive relationship between default risk

and various indicators of housing market performance. At the daily frequency, we find that

the MDRI predicts the ABX indices both in- and out-of-sample. These results suggest that

mortgage default risk tabulated through internet search queries acts as a leading indicator

of the most up-to-date, real-time measures of housing market performance. Specifically, we

find that increases in the MDRI relative to its 1-year moving average lead to lower ABX

returns. Hence, elevated levels of the MDRI predict higher costs of default risk insurance

for subprime borrowers as reflected in the ABX. Using weekly data, we further assess the

predictive relationship between the MDRI and house prices and find that increases in default

risk predict lower house price returns for a weekly index based on the Anenberg and Laufer

(2014) near real time house price index that exploits de-listing information. Similarly, at

the monthly periodicity, high levels of the MDRI lead to lower future house price returns

across both the Case-Shiller and FHFA house price methodologies. Overall, research findings

suggest the utility of internet search data in the development of timely indices of mortgage

default risk that provide leading housing market information to researchers, practitioners,

and policymakers.

25

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30

A Tables

Table 1: Google Trends Search Terms

Search Terms

Daily 1 foreclosure2 foreclosures3 foreclosure assistance4 foreclosure help5 government assistance mortgage6 home mortgage assistance7 home mortgage help8 housing assistance9 mortgage assistance program10 mortgage assistance11 mortgage foreclosure help12 mortgage foreclosure13 mortgage help

Monthly 1 foreclosure assistance2 foreclosure help3 government assistance mortgage4 home mortgage assistance5 home mortgage help6 housing assistance7 mortgage assistance program8 mortgage assistance9 mortgage foreclosure help10 mortgage foreclosure11 mortgage help

31

Tab

le2:

Dai

lyC

orre

lati

ons

–M

DR

I,V

IX,

CM

EC

S,

and

AB

XIn

dic

es

Subpri

me

Cre

dit

Def

ault

Sw

aps

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

MD

RI

VIX

CS

CM

Ere

tA

BX

AA

Are

tA

BX

AA

ret

AB

XA

ret

AB

XB

BB

ret

AB

XB

BB

-re

t

MD

RI

1.00

***

(0.0

0)V

IX0.

71**

*1.

00**

*(0

.00)

(0.0

0)C

SC

ME

ret

-0.0

7***

-0.0

7***

1.00

***

(0.0

0)(0

.00)

(0.0

0)A

BX

AA

Are

t-0

.06*

*-0

.09*

**0.

05**

1.00

***

(0.0

1)(0

.00)

(0.0

4)(0

.00)

AB

XA

Are

t-0

.07*

**-0

.10*

**0.

000.

50**

*1.

00**

*(0

.00)

(0.0

0)(0

.84)

(0.0

0)(0

.00)

AB

XA

ret

-0.0

6**

-0.0

7***

0.04

0.35

***

0.65

***

1.00

***

(0.0

3)(0

.01)

(0.2

4)(0

.00)

(0.0

0)(0

.00)

AB

XB

BB

ret

-0.0

5**

-0.0

20.

020.

23**

*0.

46**

*0.

45**

*1.

00**

*(0

.04)

(0.5

0)(0

.58)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

AB

XB

BB

-re

t-0

.04*

-0.0

10.

030.

20**

*0.

42**

*0.

42**

*0.

88**

*1.

00**

*(0

.09)

(0.7

2)(0

.28)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

Notes:

MD

RI

isth

eM

ortg

age

Def

ault

Ris

kIn

dex

;V

IXis

the

VIX

equ

ity

fear

gau

ge;

CS

CM

Ere

tare

the

retu

rns

on

the

CM

EC

ase

-Sh

ille

rh

ou

sep

rice

futu

res;

an

dA

BX

AA

Ath

rou

ghA

BX

BB

B-

retu

rns

are

the

retu

rns

onth

eA

BX

sub

pri

me

mort

gage

cred

itd

efau

ltsw

ap

s.O

ne,

two,

an

dth

ree

ast

eris

ks

rep

rese

nts

sign

ifica

nce

at

the

10,

5,

an

d1

per

cent

leve

ls,

resp

ecti

vel

y.

32

Table 3: Monthly Correlations – MDRI, Housing Sentiment, Housing Returns, Delinquencies, and Fore-closures

MDRI

Michigan Sentiment–CantAfford 0.63***(0.00)

Michigan Sentiment–UncertainFuture 0.68***(0.00)

CaseShiller US Returns -0.59***(0.00)

FHFA US Returns -0.63***(0.00)

30 Days Delinquent 0.70***(0.00)

30 Days Delinquent-Prime 0.49***(0.00)

30 Days Delinquent-Subprime 0.68***(0.00)

60 Days Delinquent 0.86***(0.00)

60 Days Delinquent-Prime 0.72***(0.00)

60 Days Delinquent-Subprime 0.85***(0.00)

90 Days Delinquent 0.42***(0.00)

90 Days Delinquent-Prime 0.36***(0.00)

90 Days Delinquent-Subprime 0.50***(0.00)

Foreclosures 0.40***(0.00)

Foreclosures-Prime 0.29***(0.00)

Foreclosures-Subprime 0.51***(0.00)

Notes: One, two, and three asterisks represents significance at the 10, 5, and 1 percent levels, respectively.

33

Tab

le4:

Dai

lyP

redic

tive

Reg

ress

ions

–A

BX

Indic

es,

Cas

e-Shille

rF

utu

res,

the

VIX

,M

edic

al,

and

the

MD

RI

Ret

urn

son

Subpri

me

Cre

dit

Def

ual

tSw

aps

(1)

(2)

(3)

(4)

(5)

(6)

(7)

AB

XA

AA

AB

XA

AA

BX

AA

BX

BB

BA

BX

BB

B-

CS

CM

E∆

VIX

MD

RI

ma t

−1

−0.

11∗∗

∗−

0.04

0.00

−0.

07−

0.16

∗∗0.

010.

01(0.0

4)(0.0

8)(0.1

0)(0.1

1)(0.0

7)(0.0

2)(0.0

2)M

DR

Im

a t−2

−0.

11−

0.15

−0.

140.

02(0.0

8)(0.1

0)(0.1

1)(0.0

3)M

DR

Im

a t−3

−0.

04(0.0

3)M

DR

Im

a t−4

−0.

03(0.0

2)

Dep

Var

Lag

s6

66

66

66

AD

SL

ag6

51

41

14

Cor

pSpre

adL

ags

11

21

11

2∆

VIX

lags

63

11

11

NA

SP

YL

ags

43

11

11

6T

reas

Spre

adL

ags

43

11

11

6U

nce

rtai

nty

Lag

s6

61

11

16

MD

RI

ma

F-s

tat

9.50

***

4.07

**3.

01**

4.28

**4.

87**

0.90

0.12

R2

0.12

0.09

0.10

0.02

0.02

0.03

0.04

Adj.

R2

0.10

0.08

0.09

0.01

0.01

0.02

0.03

Sta

rtD

ate

2006

-01-

2020

06-0

1-20

2006

-01-

2020

06-0

1-19

2006

-01-

1920

07-0

8-02

2005

-02-

24E

nd

Dat

e20

13-1

2-31

2013

-12-

3120

12-0

2-29

2012

-02-

2920

12-0

2-29

2013

-12-

3120

13-1

2-31

Num

.ob

s.19

7119

6915

1715

1715

1515

9722

07

Notes:

Dai

lyp

red

icti

vere

gres

sion

sof

the

dep

end

ent

vari

able

onth

eM

DR

Im

inu

sit

s1-y

ear

(250

Day

)m

ovin

gav

erage

an

dco

ntr

ols

.T

he

F-s

tati

stic

inth

em

idd

lepan

elte

sts

the

nu

llhyp

oth

esis

that

MD

RI

ma t−1

=···

=M

DR

Im

at−

j=

0.

Inco

lum

ns

(1)

thro

ugh

(5),

the

dep

end

ent

vari

ab

les

are

the

retu

rns

on

the

AB

Xsu

bp

rim

ecr

edit

def

au

ltsw

aps;

colu

mn

s(6

)an

d(7

)sh

owth

ere

sult

sw

hen

the

Cas

e-S

hil

ler

CM

Eh

ou

sin

gre

turn

futu

res

an

dth

efi

rst-

diff

eren

ceof

the

VIX

equ

ity

fear

gau

ge

rep

rese

nt

the

dep

end

ent

vari

able

.C

ontr

olva

riab

les

incl

ud

ela

gsof

the

AD

SB

usi

nes

sC

ond

itio

ns

Ind

ex(A

DS

),th

eco

rpora

tedef

au

ltsp

read

(Corp

Sp

read

),th

efi

rst-

diff

eren

cein

the

VIX

ind

ex(∆

VIX

),th

eT

reas

ury

Sp

read

(Tre

asS

pre

ad),

and

Eco

nom

icP

olic

yU

nce

rtain

ty(U

nce

rtain

ty).

Wh

ite

het

erosk

edast

icit

yro

bu

stst

an

dard

erro

rsare

inp

are

nth

eses

.O

ne,

two,

an

dth

ree

aste

risk

sre

pre

sents

sign

ifica

nce

atth

e10

,5,

and

1p

erce

nt

leve

ls,

resp

ecti

vely

.

34

Table 5: Weekly Predictive Regressions – Case-Shiller and Anenberg-Laufer Housing Returns and theMDRI

Housing Returns

(1) (2)

CS AL

MDRI mat−1 −0.02∗ −0.03(0.01) (0.05)

MDRI mat−2 −0.02(0.05)

MDRI mat−3 0.05(0.05)

MDRI mat−4 0.11∗∗

(0.05)MDRI mat−5 −0.10∗∗∗

(0.04)

Dep Var Lags 5 5ADS Lag 4 1CorpSpread Lags 1 1∆VIX lags 1 1SPY Lags 1 1TreasSpread Lags 1 1Uncertainty Lags 1 1

MDRI ma F-stat 3.83* 2.86**R2 0.52 0.33Adj. R2 0.49 0.28

Start Date 2008-06-13 2008-06-13End Date 2012-10-19 2012-10-19Num. obs. 223 223

Notes: See the notes for table 4. Weekly predictive regressions of the dependent variable on the weekly MDRI and controls.The weekly MDRI is computed by averaging the daily MDRI each week. CS is the Case-Shiller repeat sales house priceindex and AL is the Anenberg and Laufer near real time house price index.

35

Table 6: Monthly Predictive Regressions – Housing Sentiment, House Price Returns, and the MDRI

Michigan Sentiment Housing Returns

(1) (2) (3) (4) (5) (6)

∆CantAfford ∆UncertainFuture CaseShiller CaseShiller FHFA FHFA

MDRI mat−1 0.40∗ 0.10 −0.05 −0.04 −0.07∗∗∗ −0.07∗∗∗

(0.22) (0.20) (0.03) (0.03) (0.02) (0.02)MDRI mat−2 0.13 −0.35 −0.04 −0.04 0.02 0.04

(0.25) (0.24) (0.04) (0.04) (0.03) (0.03)MDRI mat−3 0.35∗ 0.10∗∗∗ 0.09∗∗∗

(0.20) (0.03) (0.03)MDRI mat−1*crisis −0.04 −0.01

(0.07) (0.04)MDRI mat−2*crisis −0.07 −0.08∗∗

(0.10) (0.04)MDRI mat−3*crisis −0.10

(0.08)

Dep Var Lags 7 7 3 3 5 5Afford Lags 1 6 1 1 1 1ArmApplications Lags 1 4 1 1 4 4∆BBMD30 Lags 3 2 1 1 1 1∆BBMD90 Lags 1 3 1 1 5 5CorpSpread Lags 1 7 2 2 7 7Employment Lags 1 1 1 1 6 6HouseStarts Lags 2 3 1 1 3 3INDPRO Lags 2 1 2 2 5 5Loan-to-Price Lags 1 1 1 1 1 1MortgSpread Lags 1 1 1 1 1 1RetailSales Lags 1 2 1 1 5 5TreasSpread Lags 5 2 1 1 3 3Uncertainty Lags 1 1 7 7 3 3∆VIX Lags 1 4 3 3 2 2

MDRI ma F-stat 2.45* 1.25 5.74*** 6.85*** 7.66*** 7.28***MDRI ma*crisis F-stat 3.63** 2.96*

R2 0.56 0.77 0.96 0.96 0.97 0.97Adj. R2 0.38 0.58 0.95 0.95 0.95 0.95

Start Date 2005-09-01 2005-09-01 2005-09-01 2005-09-01 2005-09-01 2005-09-01End Date 2014-12-01 2014-12-01 2014-12-01 2014-12-01 2014-12-01 2014-12-01Num. obs. 111 111 111 111 111 111

Notes: Monthly predictive regressions of the dependent variable on the MDRI minus its 1-year (12 Month) moving averageand controls. Controls include lags of the dependent variable, the Housing Affordability Index (Afford), the growth in ARMApplications (ArmApplications), the first-difference on 30 and 90 day delinquencies (∆BBMD30; ∆BBMD90), the corporatedefault spread (CorpSpread), the employment-population ratio (Employment), the log of housing starts (HouseStarts), thegrowth in industrial production (INDPRO), the log of the Loan-to-Price ratio (Loan-to-Price), the fixed rate mortgage - 30-year Treasury spread (MortgSpread), the log first-difference of retail sales (RetailSales), the Treasury Spread (TreasSpread),Economic Policy Uncertainty Index (Uncertainty), and the first-difference in the VIX index (∆VIX). The first F-statisticin the middle panel tests the null hypothesis that MDRI mat−1 = · · · = MDRI mat−j = 0. The second F-statistic in themiddle panel tests the null hypothesis that MDRI mat−1*crisis= · · · = MDRI mat−j*crisis = 0. White heteroskedasticityrobust standard errors are in parentheses. One, two, and three asterisks represent significance at the 10, 5, and 1 percentlevels, respectively.

36

Tab

le7:

Mon

thly

Pre

dic

tive

Reg

ress

ions

–D

elin

quen

cies

,F

orec

losu

res,

and

the

MD

RI

30D

ays

Del

inqu

ent

60

Day

sD

elin

qu

ent

90

Day

sD

elin

qu

ent

Fore

closu

res

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

∆T

otal

∆P

rim

e∆

Su

bp

rim

e∆

Tota

l∆

Pri

me

∆S

ub

pri

me

∆T

otal

∆P

rim

e∆

Su

bp

rim

e∆

Tota

l∆

Pri

me

∆S

ub

pri

me

MD

RI

mat−

10.

050.

11−

0.0

10.

21∗∗∗

0.2

0∗∗

0.2

4∗∗

−0.

33∗∗∗

−0.0

5−

0.00

0.0

20.0

10.1

3∗

(0.0

7)(0.0

9)(0.1

1)(0.0

7)

(0.0

9)

(0.0

9)

(0.0

9)

(0.0

9)

(0.0

8)

(0.1

0)

(0.0

7)

(0.0

8)

MD

RI

mat−

2−

0.1

6∗

0.2

2∗∗

−0.

08

0.1

50.3

3∗∗∗

(0.0

9)(0.1

0)

(0.1

1)

(0.1

1)

(0.1

1)

MD

RI

mat−

30.

120.

02

0.10

−0.

54∗∗∗

(0.0

9)(0.1

0)

(0.1

4)

(0.1

0)

MD

RI

mat−

4−

0.40∗∗∗

0.01

0.2

7∗∗∗

(0.0

7)

(0.1

3)

(0.1

0)

MD

RI

mat−

5−

0.10

−0.

14

0.15∗

(0.0

9)

(0.0

9)

(0.0

8)

MD

RI

mat−

6−

0.04

0.1

9∗∗

(0.1

0)

(0.0

9)

MD

RI

mat−

7−

0.04

0.1

9∗∗

(0.0

9)

(0.0

8)

Dep

Var

Lag

s6

67

77

72

77

72

2A

ffor

dL

ags

11

21

12

22

11

14

Arm

Ap

pli

cati

ons

Lag

s1

11

11

11

11

11

1∆

BB

MD

30L

ags

NA

11

15

31

11

11

6∆

BB

MD

90L

ags

21

11

32

NA

16

44

6C

orp

Sp

read

Lag

s3

11

43

57

11

11

1E

mp

loym

ent

Lag

s7

61

12

13

41

32

2H

ouse

Sta

rts

Lag

s1

11

34

35

41

77

5IN

DP

RO

Lag

s6

72

22

21

21

11

1L

oan

-to-

Pri

ceL

ags

11

11

11

41

44

47

Mor

tgS

pre

adL

ags

11

21

11

22

21

16

Ret

ailS

ales

Lag

s7

61

22

14

51

12

4T

reas

Sp

read

Lag

s5

13

56

53

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37

Table 8: Daily VAR Cumulative Impulse Responses

Day

1 2 3 4 5 10 15

ABX BBB- -0.50 -0.82 -1.23 -1.32 -1.41 -1.95 -2.41ABX BBB -0.56 -0.95 -1.39 -1.48 -1.64 -2.11 -2.58ABX A 0.28 0.10 -0.19 -0.46 -0.77 -1.33 -1.85ABX AA 0.28 -0.04 -0.16 -0.41 -0.70 -1.03 -1.43ABX AAA 0.04 -0.07 -0.16 -0.24 -0.32 -0.79 -1.25SP500 0.22 0.26 0.21 0.08 -0.03 -0.18 -0.36

Notes: See the notes for figure 6. Cumulative responses are computed for up to 15 days following a twostandard deviation increase in MDRI ma.

Table 9: Monthly VAR Cumulative Impulse Responses

Month

1 2 3 4 5 6 10

Case-Shiller -0.22 -0.54 -0.84 -1.08 -1.28 -1.45 -1.89FHFA -0.13 -0.24 -0.25 -0.22 -0.25 -0.32 -0.6130-Day 0.31 0.54 0.53 0.54 0.60 0.60 0.6060-Day 0.46 1.16 1.57 1.70 1.80 1.90 1.9690-Day -0.00 0.25 0.56 0.89 1.13 1.33 1.87Foreclosures -0.14 0.12 0.44 0.82 1.11 1.36 1.94Industrial Production -0.15 -0.70 -1.02 -1.14 -1.30 -1.45 -1.76

Notes: See the notes for figure 7. Cumulative responses are computed for up to 10 months following a twostandard deviation increase in MDRI ma.

38

Table 10: Robustness–Average F-Statistics from Predictive Regressions

F-Stat Mean F-Stat Mean Boot SE

Daily

ABX AAA Subprime CDS ret 10.95 0.30ABX AA Subprime CDS ret 5.84 0.20ABX A Subprime CDS ret 5.15 0.21ABX BBB Subprime CDS ret 9.23 0.49ABX BBB- Subprime CDS ret 9.24 0.37CS CME Housing Futures ret 1.75 0.13∆VIX 0.67 0.08

Weekly

CS Housing Returns 6.01 0.27AL Housing Returns 1.74 0.18

Monthly

Michigan Sentiment–∆CantAfford 1.87 0.06Michigan Sentiment–∆UncertainFuture 1.43 0.07CaseShiller US Returns 2.34 0.15FHFA US Returns 11.05 0.2030 Days Delinquent 0.76 0.0530 Days Delinquent–Prime 1.20 0.0830 Days Delinquent–Subprime 0.97 0.0560 Days Delinquent 4.92 0.2060 Days Delinquent–Prime 3.50 0.1560 Days Delinquent–Subprime 3.05 0.1590 Days Delinquent 2.10 0.1290 Days Delinquent–Prime 1.30 0.1090 Days Delinquent–Subprime 2.08 0.13Foreclosures 1.91 0.14Foreclosures–Prime 2.43 0.15Foreclosures–Subprime 1.95 0.15

Notes: The mean of the F-statistics from the regressions in the online appendix. The left column holds themean F-statistic and the right column shows the bootstrapped standard error of the average F-statistic.

39

Table 11: Reverse Causality – MDRI ma and Predictor Variables

No Controls With Controls

Daily

ABX AAA Subprime CDS ret 2.08 3.54**ABX AA Subprime CDS ret 1.48 1.53ABX A Subprime CDS ret 1.96* 2.10*ABX BBBSubprime CDS ret 1.65 1.70ABX BBB- Subprime CDS ret 1.23 1.56CME CS Housing Futures ret 0.22 0.05

Weekly

CS Housing Returns 2.67* 1.80AL Housing Returns 0.55 0.59

Monthly

Michigan Sentiment–∆CantAfford 0.03 0.15Michigan Sentiment–∆UncertainFuture 1.58 5.46***CaseShiller Returns 6.68** 14.81***FHFA Returns 1.82 3.32***30 Days Delinquent 2.96** 1.89*30 Days Delinquent-Prime 0.27 1.7830 Days Delinquent-Subprime 1.40 2.01*60 Days Delinquent 4.16*** 3.33***60 Days Delinquent-Prime 3.71*** 4.30***60 Days Delinquent-Subprime 2.40** 3.03**90 Days Delinquent 0.17 1.4590 Days Delinquent-Prime 0.21 0.4590 Days Delinquent-Subprime 0.30 1.37Foreclosures 3.47* 10.93***Foreclosures-Prime 4.40** 9.52***Foreclosures-Subprime 2.40 1.24

Notes: F-statistics from predictive regressions of MDRI ma on daily, weekly, and monthly variables. Themiddle column shows the results when only lags of the dependent variable are used as controls. The rightcolumn shows the results when exogenous controls are included in the regression. The controls are the sameas those used in tables 4, 5, and 6. Lags are chosen using the Akaike information criterion (AIC). One, two,and three asterisks represent significance at the 10, 5, and 1 percent levels, respectively. Standard errors forthe F-statistics are computed using the White heteroskedasticity robust variance-covariance matrix.

40

Table 12: Daily One-Step-Ahead Out-Of-Sample Forecast Results

Returns on Subprime Credit Default Swaps

(1) (2) (3) (4) (5) (6) (7) (8)

Window Start End ABX AAA ABX AA ABX A ABX BBB ABX BBB-

30 2006-03-06 2012-02-29 4.65 5.95** 6.28** 14.50*** 14.92***(0.13) (0.03) (0.04) (0.00) (0.00)

40 2006-03-20 2012-02-29 3.54 3.63 6.48** 10.58*** 9.58***(0.13) (0.12) (0.02) (0.00) (0.00)

60 2006-04-18 2012-02-29 2.11 3.78** 4.26** 6.74*** 5.76***(0.12) (0.02) (0.04) (0.00) (0.00)

80 2006-05-16 2012-02-29 2.29** 4.31*** 3.10 4.59*** 5.60***(0.04) (0.00) (0.11) (0.00) (0.00)

100 2006-06-14 2012-02-29 1.41 2.11* 3.01 4.09*** 3.70**(0.14) (0.06) (0.10) (0.00) (0.01)

120 2006-07-13 2012-02-29 1.88** 2.07* 1.66 3.94*** 3.90***(0.04) (0.07) (0.26) (0.00) (0.00)

140 2006-08-10 2012-02-29 1.26 1.71* 0.89 2.92** 2.92***(0.17) (0.10) (0.46) (0.01) (0.01)

Notes: The daily one-step-ahead out-of-sample R2 statistics in percentage form. Column (1) shows thetraining window (in days) and the next two columns show the starting and ending dates of the out-of-sampleperiod for each forecast window. p-values from the Diebold-Mariano test are in parentheses where the nullhypothesis is that of equal forecast performance. One, two, and three asterisks represent p-values for theDiebold-Mariano test that are less than 0.1, 0.05, and 0.01, respectively.

Table 13: Weekly One-Step-Ahead Out-Of-Sample Forecast Results

Housing Returns

(1) (2) (3) (4) (5)

Window Start End CS AL

(0.00) (0.11)40 2009-03-20 2012-10-19 6.28** 22.69

(0.02) (0.16)50 2009-05-29 2012-10-19 6.22** 16.86*

(0.03) (0.09)60 2009-08-07 2012-10-19 3.27* 11.61

(0.08) (0.15)70 2009-10-16 2012-10-19 2.56 13.54

(0.14) (0.12)

Notes: See the notes for table. The training window is listed in weeks. 12.

41

Table 14: Monthly Panel Data Predictive Regressions

OLS GMM

(1) (2) (3) (4)

reti,t−1 0.62∗∗∗ 0.62∗∗∗ 0.08∗ 0.10∗∗∗

(0.03) (0.03) (0.04) (0.04)reti,t−2 0.19∗∗∗ 0.19∗∗∗ 0.14∗∗∗ 0.14∗∗∗

(0.04) (0.04) (0.04) (0.04)reti,t−3 −0.12∗∗∗ −0.12∗∗∗ −0.08∗∗ −0.08∗∗

(0.03) (0.03) (0.04) (0.04)MDRI mai,t−1 −0.04 −0.02 −0.09∗∗ −0.05

(0.03) (0.03) (0.05) (0.05)HouseStartsi,t−1 0.32∗∗∗ 0.32∗∗∗ 0.47∗∗∗ 0.45∗∗∗

(0.09) (0.09) (0.13) (0.12)Unempi,t−1 0.02 0.02 0.12∗∗∗ 0.12∗∗∗

(0.03) (0.03) (0.03) (0.03)MDRI mai,t−1*crisis −0.05 −0.14∗

(0.05) (0.08)

R2 0.78 0.78Adj. R2 0.76 0.76City FE Yes Yes Yes YesMonth FE Yes YesTime FE Yes Yes

Notes: Predictive Panel Data Regressions of the dependent variable on the city-level MDRI minus its 1-year (12 Month) moving average and controls. The left panel shows OLS estimates with Driscoll and Kraay(1998) robust standard errors in parentheses. In the right-panel, we show the Arellano and Bond (1991)GMM estimates; Windmeijer (2005) robust standard errors are in parentheses. One, two, and three asterisksrepresent significance at the 15, 10, and 5 percent levels, respectively.

42

B Figures

Figure 1: Google Searches for “mortgage assistance”

Figure 2: Google Searches for “government mortgage help”

43

Figure 3: The Daily MDRI and Housing Market Indices

−500

50100150

20406080

100

0255075

100

0255075

100

160

180

200

220

−4−3−2−1

01

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pread

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44

Figure 4: The Monthly MDRI and Housing Market Indices

0

2

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Notes: Plots of the standardized monthly MDRI, 20-City Case-Shiller returns, UncertainFuture, 30 daydelinquency, and foreclosure indices. The shaded bar is a bear market defined as a 20 percent or more dropin the S&P500 over two or more months.

45

Figure 5: City-Level MDRIs

0

100

200

300

2006 2008 2010 2012 2014time

Per

cent

age

Incr

ease

from

200

6M01

MDRI Dallas

MDRI Denver

MDRI Miami

MDRI Phoenix

Notes: Plots of cumulative percentage increase in the monthly MDRIs for select cities. The shaded bar is abear market defined as a 20 percent or more drop in the S&P500 over two or more months.

46

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51

D Appendix: The ABX series

In this appendix we briefly describe the ABX indices. Each ABX index tracks the cost toinsure a basket of 20 subprime mortgage backed securities, equally weighted.

The ABX indices are split up based on investment quality and time of issuance. The2006-01 set of AAA and lower-rated ABX indices that we use in this paper is comprised ofloans made in the first half of 2006. We can interpret (100− ABX) as the upfront paymentabove the coupon required to insure certain mortgage loans.

To exactly understand how the ABX relates to the cost for insurance we first define thefollowing variables:

• The value for the ABX index (ABX). The ABX is always 100 on the day of issuance.

• The Loan: The amount of mortgage backed securities to be insured.

• The Coupon: The annual fixed payment for the insurance, reported in basis points.

• The Factor: The proportion of the principal currently outstanding. This equals one onthe day of issuance.

Using the above variables we can calculate the cost to insure a given amount of mortgagebacked securities:

Insurance Cost = (100− ABX) · Loan · Factor + Loan · Factor · Coupon

= (100− ABX + Coupon) · Loan · Factor (4)

The derivative of equation 4 with respect to ABX is negative. Hence, it becomes morecostly to insure mortgage backed securities as ABX falls. In other words, the ABX indicesfall as investors become more pessimistic about mortgage backed securities. The “on the roll”ABX index returns that we use in this paper refer to the most recent instance of the ABXindices. For further details on the ABX indices see Longstaff (2010). For further details onCDOs over the housing boom and bust see Cordell et al. (2011).

E Appendix: Google Trends and the Construction of the MDRI

In this appendix, we provide further details on the Google Trends Search volume indices andthe MDRI.

E.1 Appendix: Google Trends Search Volume Index (SVI) and the Construction

of the Daily MDRI

Within each sample period, the SVI for each search term is normalized to range from 0 to100 and so that a value of 100 represents the date that the given search term achieves peakrelative search volume. Indeed, the SVIs are normalized so that the SVI for every searchterm will achieve a value of 100 over a given sample period. Further, note that Google Trendsimplements a privacy filter and thus only reports the SVI when the number of absolutesearches in a given time period are above a certain unknown threshold. If the number ofsearches does not exceed this threshold, Google Trends reports a value of 0 in the SVI.Hence, zero values in the SVI represent periods when the number of searches does not meetthe Google Trends privacy threshold. Last, the Google Trends data represent a sample ofoverall Google search data. Therefore, as noted by Choi and Varian (2012), the data from

52

Google can “vary a few percent from day to day.” We did not find any substantive differencesin the Google Trends data that were downloaded on different days.41

E.2 Appendix: Construction of the Daily MDRI

At the daily frequency, Google Trends reports the data only for three months at a time. So,for every month, we download the data for three months and use the only the last monthand the last day of the middle month. To make the daily data comparable across months,we then take the log first-difference of the data within each month. After this process, forevery month, we have the log first difference in MDRI. Then, to build a levels index, weaccumulate the log first-differenced data over the full sample period (cumulative sum the logfirst differenced data). The cumulative MDRI in levels is then the percentage growth in theMDRI from the first period. Note that we only retain NYSE market days in constructionof the daily MDRI index. The levels daily MDRI thus represents the growth in mortgagedefault risk from the first available data date, March 1, 2004.

41As a robustness check, we re-downloaded the data for the monthly MDRI on several different days. Theminimum correlation coefficient between the MDRIs constructed using data from different days was 0.98.

53

F Appendix: Robustness Checks (For Online Publication Only)

Here, we discuss the rubustness of our results where we iteratively add control variables and use the AIC andthe BIC for lag length selection. To facilitate the discussion of appendix tables, we first describe format of thetables using the top panel of table 16. Here, the dependent variable is the daily ABX AAA returns and theestimates in table cells are the F-statistics corresponding to Granger Causality F-tests where the null is thatMDRI mat−1 = · · · = MDRI mat−j = 0. Within each column, each subsequent row represents the inclusion ofan additional control variable. Reading down the first column, we start with no controls (simply a regressionof the ABX AAA returns on lags of the ABX AAA returns and MDRI ma) and obtain an F-statistic of 12.27.Then, in the second row of the first column, we add the ADS index as a control and obtain an F-statistic of7.72. The regression corresponding to the third row in the first column employs the ADS index and CorpSpreadas controls; so on and so forth. In total, our results are similar to those described above with a few exceptions:First, for the weekly data, the predictive effects of the MDRI on the AL house price returns only persist whenusing the AIC for lag length selection. As the number of lags chosen by the BIC is less than or equal to thatchosen by the AIC, this suggests that the longer lags selected by the AIC are important for the predictiverelationship between the MDRI and the weekly AL house price returns. Similarly, when monthly Case-Shillerreturns or 60-day subprime delinquencies represent the dependent variable, the results are less robust whenthe BIC is used for lag length selection. Hence, longer MDRI ma lags may be important in the prediction ofthese variables.

F.1 Daily Robustness Checks

54

Table 16: Daily Robustness Checks using AIC

ABX AAA

None 12.27***ADS 7.72*** 7.72***CorpSpread 7.93*** 7.93*** 11.58***dVIX 8.27*** 8.27*** 11.78*** 12.42***SPY 8.70*** 8.70*** 12.21*** 12.82*** 13.02***TreasSpread 8.75*** 8.75*** 11.56*** 13.56*** 13.82*** 13.00***Uncertainty 9.50*** 9.49*** 12.55*** 14.09*** 14.38*** 13.56*** 14.64***

ABX AA

None 5.10***ADS 3.29** 3.29**CorpSpread 4.56** 4.56** 5.38***dVIX 4.85*** 4.85*** 5.65*** 5.42***SPY 4.89*** 4.89*** 5.67*** 5.44*** 5.44***TreasSpread 3.86** 3.86** 4.21** 5.08*** 5.10*** 4.74***Uncertainty 4.07** 4.07** 4.55** 5.29*** 5.30*** 4.92*** 6.15***

ABX A

None 4.80***ADS 3.51** 3.51**CorpSpread 3.27** 3.27** 5.29***dVIX 3.31** 3.31** 5.29*** 4.79***SPY 3.59** 3.59** 5.37*** 4.91*** 4.86***TreasSpread 2.92* 2.92* 3.91** 4.23** 4.20** 4.18**Uncertainty 3.01** 3.01** 4.06** 4.25** 4.19** 4.17** 4.67***

ABX BBB

None 8.15***ADS 7.34*** 7.34***CorpSpread 5.57*** 5.57*** 8.17***dVIX 5.60*** 5.60*** 8.17*** 8.13***SPY 5.61*** 5.61*** 8.19*** 8.16*** 8.17***TreasSpread 4.18** 4.18** 5.54*** 5.95*** 5.95*** 5.96***Uncertainty 4.28** 4.28** 5.61*** 5.94*** 5.93*** 5.94*** 7.83***

ABX BBB-

None 10.92***ADS 9.70*** 9.70***CorpSpread 7.02*** 7.02*** 11.17***dVIX 7.04*** 7.04*** 11.15*** 10.89***SPY 7.02*** 7.02*** 11.16*** 10.90*** 10.93***TreasSpread 4.75** 4.75** 6.86*** 7.26*** 7.28*** 7.29***Uncertainty 4.87** 4.87** 6.94*** 7.15*** 7.17*** 7.18*** 10.56***

Notes: Table continued on next page. Each panel shows the Granger Causality F-statistics from a regression of the givendependent variable on MDRI ma and controls. The null hypothesis for the F-test is that all of the coefficients on MDRI mat−jare jointly equal to zero. In each column, the control variables are added to the model sequentially as you move down the column.Lags for the predictors are chosen based on the Akaike information criterion (AIC). One, two, and three asterisks representsignificance at the 15, 10, and 5 percent levels using the the White heteroskedasticity robust variance covariance matrix.

55

CS CME

None 1.05ADS 0.72 0.72CorpSpread 0.85 0.85 0.84dVIX 0.85 0.85 0.84 1.05SPY 0.88 0.88 0.88 1.05 1.05TreasSpread 0.86 0.86 0.86 1 1 1Uncertainty 0.90 0.9 0.89 1.03 1.02 1.03 1.09

∆VIX

None 1.27ADS 1.23 1.23CorpSpread 0.08 0.08 1.01dVIXSPY 0.02 0.02 0.78 1.09TreasSpread 0.12 0.12 1.08 0.94 1.11Uncertainty 0.12 0.12 1.08 1.06 1.25 1.6

56

Table 17: Daily Robustness Checks using BIC

ABX AAA

None 12.27***ADS 7.72*** 7.72***CorpSpread 8.15*** 8.15*** 11.96***dVIX 8.35*** 8.35*** 11.85*** 12.18***SPY 8.83*** 8.83*** 11.98*** 12.29*** 12.33***TreasSpread 9.02*** 9.02*** 11.84*** 13.86*** 13.87*** 13.66***Uncertainty 8.80*** 8.80*** 11.59*** 14.01*** 14.03*** 13.82*** 10.87***

ABX AA

None 7.83***ADS 4.78** 4.78**CorpSpread 6.07** 6.07** 7.77***dVIX 6.56** 6.56** 8.26*** 8.36***SPY 6.82*** 6.82*** 8.49*** 8.53*** 8.53***TreasSpread 5.25** 5.25** 6.20** 7.86*** 7.90*** 7.16***Uncertainty 5.19** 5.19** 6.14** 7.86*** 7.89*** 7.22*** 7.40***

ABX A

None 7.90***ADS 6.12** 6.12**CorpSpread 4.99** 4.99** 8.82***dVIX 5.05** 5.05** 8.78*** 7.83***SPY 5.56** 5.56** 8.95*** 8.08*** 7.98***TreasSpread 3.97** 3.97** 5.89** 6.43** 6.35** 6.37**Uncertainty 4.21** 4.21** 6.21** 6.48** 6.36** 6.37** 7.60***

ABX BBB

None 15.85***ADS 13.68*** 13.68***CorpSpread 10.27*** 10.27*** 16.30***dVIX 10.28*** 10.28*** 16.27*** 15.82***SPY 10.35*** 10.35*** 16.34*** 15.91*** 15.88***TreasSpread 7.58*** 7.58*** 10.90*** 11.68*** 11.63*** 11.64***Uncertainty 7.96*** 7.96*** 11.31*** 11.79*** 11.73*** 11.73*** 15.12***

ABX BBB-

None 14.03***ADS 12.12*** 12.12***CorpSpread 8.88*** 8.88*** 14.40***dVIX 8.90*** 8.90*** 14.36*** 13.98***SPY 8.87*** 8.87*** 14.37*** 14.01*** 14.05***TreasSpread 6.11** 6.11** 9.06*** 9.74*** 9.75*** 9.76***Uncertainty 6.30** 6.30** 9.24*** 9.64*** 9.67*** 9.67*** 13.55***

Notes: Table continued on next page. See the notes for table 16. Lags for the predictor variables are chosen based Bayesianinformation criterion (BIC).

57

CS CME

None 3.45*ADS 1.17 1.17CorpSpread 2.09 2.09 1.99dVIX 2.09 2.09 1.99 3.45*SPY 2.08 2.08 2 3.46* 3.46*TreasSpread 2.17 2.17 2.12 3.48* 3.47* 3.46*Uncertainty 2.32 2.32 2.28 3.49* 3.49* 3.48* 3.56*

dVIX

None 0.85ADS 0.84 0.84CorpSpread 0.01 0.01 0.79dVIXSPY 0 0 0.74 1TreasSpread 0.09 0.09 1.25 1.09 0.92Uncertainty 0.09 0.09 1.25 1.14 1 0.72

58

F.2 Weekly Robustness Checks

Table 18: Weekly Robustness Checks using AIC

CS

None 7.88***ADS 3.32* 3.32*CorpSpread 2.95* 2.95* 6.21**dVIX 3.22* 3.22* 6.85*** 8.61***SPY 2.99* 2.99* 6.25** 7.72*** 7.71***TreasSpread 4.77** 4.77** 8.51*** 8.81*** 8.55*** 8.70***Uncertainty 3.83* 3.83* 7.33*** 7.54*** 7.25*** 7.13*** 6.02**

AL

None 3.28***ADS 2.68** 2.68**CorpSpread 2.70** 2.70** 2.72**dVIX 2.64** 2.64** 2.68** 3.27***SPY 2.69** 2.69** 2.71** 3.29*** 3.26***TreasSpread 2.76** 2.76** 2.95** 3.64*** 3.63*** 3.64***Uncertainty 2.86** 2.86** 2.99** 3.59*** 3.59*** 3.64*** 3.26***

Notes: See the notes for table 16.

Table 19: Weekly Robustness Checks using BIC

CS

None 7.88***ADS 3.77* 3.77*CorpSpread 3.72* 3.72* 6.21**dVIX 4.13** 4.13** 6.85*** 8.61***SPY 4.02** 4.02** 6.25** 7.72*** 7.71***TreasSpread 5.84** 5.84** 8.51*** 8.81*** 8.55*** 8.70***Uncertainty 4.77** 4.77** 7.33*** 7.54*** 7.25*** 7.13*** 6.02**

AL

None 0.97ADS 0.05 0.05CorpSpread 0.03 0.03 0.06dVIX 0.02 0.02 0.09 1.1SPY 0.02 0.02 0.09 1.09 1.06TreasSpread 0.00 0.00 0.31 1.62 1.55 1.42Uncertainty 0.00 0.00 0.21 0.92 0.87 0.76 0.46

59

F.3 Monthly Robustness Checks

60

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ad

8.8

1***

8.8

1***

8.8

4***

∆B

BM

D30

9.3

6***

9.3

6***

9.2

6***

9.9

0***

∆B

BM

D90

7.2

0***

7.2

0***

7.1

5***

12.5

3***

12.8

9***

∆V

IX7.6

2***

7.6

2***

7.6

6***

14.9

8***

15.0

5***

13.0

6***

Em

plo

ym

ent

11.5

3***

11.5

3***

11.2

9***

22.6

2***

22.5

2***

12.4

1***

10.1

1***

House

Sta

rts

11.2

6***

11.2

6***

11.2

6***

23.9

4***

23.7

9***

13.7

0***

11.2

5***

10.4

5***

IND

PR

O8.4

4***

8.4

4***

8.4

2***

13.5

5***

13.4

4***

8.6

1***

7.1

5***

6.6

8***

7.5

6***

Reta

ilSale

s10.7

8***

10.7

8***

10.7

6***

17.8

4***

17.7

2***

9.7

3***

8.6

1***

7.9

8***

9.3

0***

13.5

1***

Tre

asS

pre

ad

11.3

5***

11.3

5***

11.4

2***

17.2

0***

17.2

3***

9.7

3***

8.7

4***

7.5

2***

7.7

2***

12.0

4***

10.5

9***

Uncert

ain

ty7.4

3***

7.4

3***

7.3

7***

10.2

6***

10.1

8***

5.8

4***

5.4

4***

5.1

5***

4.7

1**

9.7

7***

9.0

5***

8.5

5***

Loan-t

o-P

rice

7.1

4***

7.1

4***

6.9

8***

9.5

1***

9.4

5***

5.5

5***

5.1

1***

5.4

7***

5.0

9***

9.8

5***

9.1

6***

8.4

9***

10.5

3***

Mort

gSpre

ad

Lags

7.1

5***

7.1

5***

7.0

2***

9.3

0***

9.2

2***

5.0

9***

4.6

4**

4.4

8**

4.3

3**

9.9

9***

9.2

1***

9.0

3***

10.1

7***

10.3

9***

Arm

Applicati

ons

7.6

6***

7.6

6***

7.5

3***

9.5

0***

9.5

7***

4.7

4**

4.3

5**

4.2

9**

3.9

0**

7.8

2***

7.9

3***

7.3

6***

9.4

5***

9.4

7***

10.0

0***

Notes:

Table

continued

on

thenexttw

opages.

See

the

note

sfr

om

tab

le16.

61

30

Days

Delinquent–

All

(∆B

BM

D30)

None

1.2

1A

fford

1.1

31.1

3C

orp

Spre

ad

1.0

61.0

61.3

6∆

BB

MD

90

1.1

1.1

1.4

1.2

6∆

VIX

1.4

1.4

1.7

61.1

11.1

8E

mplo

ym

ent

0.6

10.6

10.9

1.0

31.3

41.6

7H

ouse

Sta

rts

0.6

10.6

10.9

30.8

61.1

1.3

51.5

9IN

DP

RO

0.0

10.0

10

0.0

30

00.1

50.3

5R

eta

ilSale

s0.0

80.0

80.1

20.0

50.1

0.1

60.7

10.9

91.2

9T

reasS

pre

ad

0.8

40.8

40.8

50.5

20.7

51

2.2

21.7

62.5

32.8

5*

Uncert

ain

ty1

11.0

80.9

21.2

31.5

32.6

21.7

42.5

62.8

4*

1.8

4L

oan-t

o-P

rice

0.4

30.4

30.4

70.3

60.5

50.6

92.2

21.4

82.3

63.1

1*

2.0

41.5

9M

ort

gSpre

ad

Lags

0.4

90.4

90.5

50.5

10.7

20.8

61.9

41.2

2.0

21.1

60.8

40.6

60.4

5A

rmA

pplicati

ons

0.3

80.3

80.4

10.3

60.6

0.7

21.9

51.1

62.0

61.2

41

0.7

50.5

31.3

5

30

Days

Delinquent–

Pri

me

(∆B

BM

DP

30)

None

4.3

7***

Aff

ord

4.4

0***

4.4

0***

Corp

Spre

ad

3.8

5**

3.8

5**

3.9

8**

∆B

BM

D90

4.3

1***

4.3

1***

4.4

7***

4.6

7***

∆V

IX4.1

2***

4.1

2***

4.2

6***

4.2

7***

4.0

9***

Em

plo

ym

ent

1.0

41.0

41.1

81.5

81.5

11.5

9H

ouse

Sta

rts

1.1

1.1

1.2

41.5

91.5

11.5

93.3

5**

IND

PR

O1.3

11.3

11.2

41.1

81.2

71.2

61.4

32.4

6*

Reta

ilSale

s1.0

81.0

81.0

51.0

41.1

31.1

91.1

32.0

33.1

4**

Tre

asS

pre

ad

1.0

91.0

91.0

81.0

71.1

71.2

41.1

1.1

72.3

1*

3.1

6**

Uncert

ain

ty1.0

81.0

81.1

31.1

21.2

11.2

71.2

11.1

82.3

3*

3.2

7**

3.9

8**

Loan-t

o-P

rice

0.8

60.8

60.8

50.8

50.9

20.8

81.2

11.1

92.4

3*

3.5

8**

4.1

4***

4.1

5***

Mort

gSpre

ad

Lags

0.8

70.8

70.8

80.8

70.9

40.8

90.7

70.7

51.5

61.5

71.5

41.4

71.4

Arm

Applicati

ons

1.1

91.1

91.3

1.2

81.3

81.3

60.8

60.8

51.5

61.5

91.5

71.5

11.4

44.3

3***

30

Days

Delinquent–

Subpri

me

(∆B

BM

DS30)

None

1.2

3A

fford

0.9

0.9

Corp

Spre

ad

2.0

02.0

02.4

6∆

BB

MD

90

2.2

82.2

82.5

91.1

1∆

VIX

1.5

91.5

91.8

20.5

10.6

6E

mplo

ym

ent

0.4

60.4

60.6

50.0

60.0

90.3

House

Sta

rts

0.6

30.6

30.8

0.6

90.6

81.1

41.6

IND

PR

O1.0

21.0

21.1

1.1

41.0

81.7

62.2

93.0

2*

Reta

ilSale

s0.9

70.9

71.0

31.0

50.9

81.2

51.5

72.1

80.5

6T

reasS

pre

ad

0.2

70.2

70.6

40.6

50.6

1.0

51.1

71.0

30.2

0.4

7U

ncert

ain

ty0.6

80.6

80.9

50.9

70.9

81.6

31.5

21.5

20.7

1.2

62.4

9L

oan-t

o-P

rice

0.0

60.0

60.2

30.3

50.3

50.6

60.7

60.8

0.5

1.0

82.2

11.2

1M

ort

gSpre

ad

Lags

0.0

00

0.0

40.0

40.0

20.2

40.1

60.1

60

00.1

10.0

30.0

2A

rmA

pplicati

ons

0.0

20.0

90.0

40.0

40.0

30.2

30.1

50.1

50

00.1

10.0

40.0

21.2

3

62

60

Days

Delinquent–

All

(∆B

BM

D60)

None

6.0

0**

Aff

ord

6.1

0**

6.1

0**

Corp

Spre

ad

5.3

2**

5.3

2**

5.2

6**

∆B

BM

D30

6.1

5**

6.1

5**

6.1

1**

6.5

1**

∆B

BM

D90

6.3

1**

6.3

1**

6.2

3**

5.4

6**

5.0

4**

∆V

IX8.1

5***

8.1

5***

7.7

7***

4.9

8**

4.6

7**

5.2

7**

Em

plo

ym

ent

8.1

1***

8.1

1***

7.8

2***

7.8

3***

7.3

3***

7.4

3***

7.3

8***

House

Sta

rts

8.0

1***

8.0

1***

7.8

4***

6.8

7**

6.3

9**

6.7

2**

6.9

0**

6.9

1***

IND

PR

O8.5

4***

8.5

4***

8.4

0***

8.2

6***

7.3

4***

7.6

7***

7.8

6***

7.6

8***

8.7

6***

Reta

ilSale

s9.1

0***

9.1

0***

8.8

1***

7.3

0***

6.2

0**

7.2

5***

7.9

2***

7.6

8***

9.0

4***

3.8

6*

Tre

asS

pre

ad

7.9

3***

7.9

3***

7.5

3***

6.9

2**

5.6

4**

7.0

9***

8.9

2***

9.0

5***

9.6

2***

8.6

9***

12.4

4***

Uncert

ain

ty10.9

0***

10.9

0***

9.9

7***

8.5

8***

7.4

4***

8.9

4***

10.8

7***

11.3

1***

11.3

2***

10.2

0***

13.9

3***

10.2

6***

Loan-t

o-P

rice

7.3

9***

7.3

9***

6.3

1**

7.9

1***

6.8

9**

6.1

4**

7.0

9***

9.3

9***

8.5

7***

9.4

7***

13.5

8***

10.1

2***

6.6

9**

Mort

gSpre

ad

Lags

7.8

6***

7.8

6***

7.1

6***

7.2

7***

6.3

9**

5.8

6**

6.4

4**

7.6

7***

6.7

7**

6.5

3**

8.0

1***

8.3

0***

6.1

9**

5.5

1**

Arm

Applicati

ons

8.3

7***

8.3

7***

7.9

2***

7.6

4***

6.6

7**

6.1

3**

6.7

8**

8.1

1***

7.0

6***

6.9

6***

8.4

1***

9.3

6***

6.6

4**

5.9

2**

6.5

3**

60

Day

Delinquent–

Pri

me

(∆B

BM

DP

60)

None

7.1

3***

Aff

ord

7.6

9***

7.6

9***

Corp

Spre

ad

5.0

3**

5.0

3**

4.7

1**

∆B

BM

D30

3.9

6**

3.9

6**

3.7

5*

5.4

6**

∆B

BM

D90

6.7

3**

6.7

3**

6.2

9**

6.7

6**

7.9

9***

∆V

IX6.5

8**

6.5

8**

5.9

2**

4.3

3**

5.2

6**

5.8

2**

Em

plo

ym

ent

7.2

4***

7.2

4***

6.4

8**

4.8

9**

5.1

5**

6.0

6**

7.3

3***

House

Sta

rts

6.1

0**

6.1

0**

5.6

4**

3.8

4*

4.2

8**

5.7

3**

6.7

0**

5.1

9**

IND

PR

O5.2

6**

5.2

6**

4.9

3**

3.6

4*

4.0

5**

4.9

7**

6.2

9**

5.6

9**

6.9

4***

Reta

ilSale

s5.6

7**

5.6

7**

5.1

7**

3.3

0*

3.8

5*

4.6

4**

5.4

6**

5.0

5**

6.0

7**

4.6

5**

Tre

asS

pre

ad

6.0

1**

6.0

1**

5.4

6**

3.9

2*

3.3

0*

5.6

6**

7.7

2***

7.0

4***

6.4

9**

5.9

2**

9.4

8***

Uncert

ain

ty4.9

6**

4.9

6**

4.6

2**

3.2

4*

2.6

53.7

9*

5.3

1**

5.3

0**

6.0

5**

5.3

0**

8.3

2***

7.2

0***

Loan-t

o-P

rice

3.9

1*

3.9

1*

3.5

7*

2.8

0*

1.9

21.5

31.9

93.6

8*

4.2

6**

3.8

5*

6.8

3**

5.6

7**

5.6

6**

Mort

gSpre

ad

Lags

4.5

2**

4.5

2**

4.4

0**

3.1

8*

2.4

71.6

41.9

12.6

92.5

71.9

62.8

4*

3.1

3*

4.3

6**

5.1

0**

Arm

Applicati

ons

4.8

7**

4.8

7**

4.7

8**

3.5

2*

2.6

71.8

12.1

62.9

1*

2.6

92.0

82.9

9*

3.3

1*

4.5

7**

5.3

0**

7.4

0***

60

Days

Delinquent-

Subpri

me

(∆B

BM

DS60)

None

2.5

1A

fford

2.2

2.2

Corp

Spre

ad

4.8

2**

4.8

2**

6.1

7**

∆B

BM

D30

4.4

1**

4.4

1**

5.1

1**

1.4

2∆

BB

MD

90

4.2

0**

4.2

0**

4.9

9**

1.3

92.4

∆V

IX4.5

6**

4.5

6**

5.0

9**

0.7

61.2

91.4

9E

mplo

ym

ent

3.1

9*

3.1

9*

3.7

9*

0.6

11

1.2

82.1

8H

ouse

Sta

rts

3.2

8*

3.2

8*

3.6

1*

1.2

11.5

32.1

62.8

3*

3.2

1*

IND

PR

O4.4

3**

4.4

3**

4.3

5**

2.5

92.4

63.4

3*

4.3

9**

4.7

5**

5.1

0**

Reta

ilSale

s4.2

3**

4.2

3**

4.1

1**

2.3

12.2

73.1

4*

3.4

0*

3.7

2*

3.7

4*

1.4

4T

reasS

pre

ad

3.9

0*

3.9

0*

5.4

4**

3.3

6*

2.9

7*

4.1

1**

5.2

1**

4.8

4**

5.1

8**

5.0

7**

7.8

6***

Uncert

ain

ty7.6

4***

7.6

4***

7.6

6***

6.1

0**

5.4

3**

7.3

0***

8.7

5***

7.4

8***

7.0

2***

8.0

3***

10.9

5***

7.1

4***

Loan-t

o-P

rice

5.5

7**

5.5

7**

5.6

1**

5.2

3**

4.5

4**

5.1

7**

5.7

6**

5.9

0**

5.3

1**

9.2

5***

12.4

7***

8.6

4***

3.9

4**

Mort

gSpre

ad

Lags

6.0

4**

6.0

4**

6.4

7**

4.6

2**

3.9

8**

4.5

2**

4.9

0**

3.7

3*

2.9

4*

3.2

3*

3.7

3*

3.3

8*

1.1

20.7

4A

rmA

pplicati

ons

6.9

5**

6.9

5**

6.8

7**

5.0

3**

4.2

7**

4.8

7**

5.3

0**

4.0

3**

3.0

5*

3.5

0*

3.9

8**

3.8

1*

1.1

90.8

12.6

4

63

90

Days

Delinquent–

All

(∆B

BM

D90)

None

3.0

2***

Aff

ord

3.0

0***

3.0

0***

Corp

Spre

ad

4.2

5***

4.2

5***

3.7

2***

∆B

BM

D30

4.3

3***

4.3

3***

3.7

8***

3.0

2***

∆V

IX4.9

8***

4.9

8***

4.2

9***

5.0

7***

5.0

4***

Em

plo

ym

ent

3.7

1***

3.7

1***

3.1

8***

3.4

0***

3.4

3***

2.4

1**

House

Sta

rts

4.1

5***

4.1

5***

3.8

9***

3.6

6***

3.6

9***

2.3

1**

2.8

2**

IND

PR

O3.9

0***

3.9

0***

3.7

4***

3.5

5***

3.6

1***

2.2

5**

2.6

5**

2.7

2**

Reta

ilSale

s3.7

3***

3.7

3***

3.6

1***

2.9

8***

3.1

2***

2.6

4**

2.7

0**

2.8

1**

2.8

9***

Tre

asS

pre

ad

3.9

9***

3.9

9***

4.1

4***

3.4

8***

3.5

6***

2.5

8**

2.2

1**

2.3

8**

2.4

5**

2.8

7***

Uncert

ain

ty4.5

9***

4.5

9***

5.0

5***

4.1

0***

3.9

6***

2.8

3**

2.5

5**

2.5

0**

2.4

2**

3.1

4***

3.3

7***

Loan-t

o-P

rice

6.2

9***

6.2

9***

6.4

4***

4.2

4***

3.9

0***

2.6

4**

2.3

4**

2.5

9**

2.3

5**

3.3

4***

3.5

0***

4.0

2***

Mort

gSpre

ad

Lags

6.9

0***

6.9

0***

6.9

9***

4.7

9***

4.0

5***

2.7

1**

1.7

21.8

8*

2.0

9*

1.8

3*

1.3

82.4

2**

1.7

Arm

Applicati

ons

6.6

0***

6.6

0***

6.3

3***

4.8

9***

4.1

1***

2.8

2**

1.6

71.9

0*

2.1

5**

1.8

1*

1.3

82.4

4**

1.7

13.0

3***

90

Days

Delinquent–

Pri

me

(∆B

BM

DP

90)

None

4.7

6**

Aff

ord

6.5

0**

6.5

0**

Corp

Spre

ad

3.4

2*

3.4

2*

1.8

∆B

BM

D30

3.5

8*

3.5

8*

1.9

14.9

9**

∆V

IX3.9

4*

3.9

4*

2.2

95.5

7**

5.3

2**

Em

plo

ym

ent

0.6

0.6

0.0

60.4

70.4

30.2

7H

ouse

Sta

rts

0.5

20.5

20.0

30.3

0.2

70.1

82.9

1*

IND

PR

O0.1

0.1

0.1

20.0

90.0

90.1

30.1

70.6

5R

eta

ilSale

s0.0

90.0

90.2

90.2

90.2

90.5

10

0.1

41.6

1T

reasS

pre

ad

00

0.7

20.7

20.7

20.9

90.0

80.1

0.1

3.7

3*

Uncert

ain

ty0

00.8

40.8

40.8

41.1

40.1

0.1

0.0

93.1

4*

4.8

4**

Loan-t

o-P

rice

0.3

20.3

22.1

52.1

22.1

32.5

50.7

50.6

90.0

11.9

73.4

4*

3.4

7*

Mort

gSpre

ad

Lags

0.9

30.9

33.5

1*

3.4

1*

3.4

3*

3.4

8*

2.3

92.0

21.2

80.6

40.1

70.2

10.3

Arm

Applicati

ons

0.4

50.4

53.8

8*

3.7

8*

3.7

9*

3.8

3*

2.5

82.0

91.3

70.6

40.1

70.2

20.3

14.5

6**

90

Days

Delinquent–

Subpri

me

(∆B

BM

DS90)

None

4.8

0***

Aff

ord

4.7

5***

4.7

5***

Corp

Spre

ad

4.0

4***

4.0

4***

3.7

8***

∆B

BM

D30

3.9

4***

3.9

4***

3.7

7***

4.6

9***

∆V

IX3.9

2***

3.9

2***

3.7

7***

4.7

0***

4.8

1***

Em

plo

ym

ent

3.8

1***

3.8

1***

3.3

5***

4.5

2***

4.6

3***

4.6

0***

House

Sta

rts

3.6

9***

3.6

9***

3.1

0***

3.5

4***

3.6

0***

3.5

9***

4.8

8***

IND

PR

O3.6

2***

3.6

2***

3.0

6***

3.6

1***

3.6

6***

3.6

6***

5.0

6***

4.9

7***

Reta

ilSale

s3.2

9***

3.2

9***

2.8

1**

3.3

5***

3.4

6***

3.4

7***

4.7

1***

4.6

5***

4.5

8***

Tre

asS

pre

ad

3.2

4***

3.2

4***

2.9

9***

3.7

0***

3.8

4***

3.7

0***

4.1

2***

5.1

4***

5.1

2***

5.0

7***

Uncert

ain

ty3.0

4***

3.0

4***

3.5

6***

3.7

9***

3.9

2***

3.5

5***

3.7

7***

3.7

8***

3.6

2***

3.7

6***

4.2

7***

Loan-t

o-P

rice

3.5

2***

3.5

2***

4.4

7***

4.5

8***

4.7

8***

4.4

7***

4.6

7***

4.7

0***

4.4

8***

4.8

4***

5.3

7***

6.6

2***

Mort

gSpre

ad

Lags

3.1

8***

3.1

8***

3.5

0***

3.5

9***

3.6

7***

3.4

0***

3.4

6***

3.3

8***

3.4

4***

3.7

6***

2.9

6***

4.6

9***

3.2

8***

Arm

Applicati

ons

3.0

1***

3.0

1***

3.5

1***

3.6

2***

3.7

0***

3.5

0***

3.5

5***

3.4

7***

3.5

2***

3.7

8***

2.9

5***

4.8

5***

3.4

6***

4.9

3***

64

Fore

clo

sure

s–A

ll(d

BB

MD

FC

L)

None

6.4

8***

Aff

ord

5.0

7***

5.0

7***

Corp

Spre

ad

3.7

0**

3.7

0**

3.9

9**

∆B

BM

D30

3.6

0**

3.6

0**

3.7

9**

6.2

3***

∆B

BM

D90

3.3

2**

3.3

2**

3.3

1**

5.0

8***

5.6

8***

∆V

IX3.4

5**

3.4

5**

3.5

0**

4.9

0***

5.4

1***

6.3

2***

Em

plo

ym

ent

1.8

71.8

71.6

91.5

1.5

22

2.1

3H

ouse

Sta

rts

2.1

52.1

51.8

60.6

50.6

41.0

31.1

51.1

5IN

DP

RO

2.3

22.3

21.9

10.8

0.7

91.5

61.5

61.7

15.2

0***

Reta

ilSale

s2.6

9*

2.6

9*

2.4

1*

1.4

31.4

22.7

0*

2.9

5*

3.2

7**

5.8

4***

6.8

9***

Tre

asS

pre

ad

2.6

5*

2.6

5*

2.3

9*

1.4

91.4

82.7

2*

2.9

7*

3.2

0**

5.4

9***

6.6

4***

6.2

1***

Uncert

ain

ty2.5

9*

2.5

9*

2.3

51.4

71.4

62.7

8*

3.0

1*

3.3

2**

5.5

0***

6.6

1***

6.1

3***

6.5

1***

Loan-t

o-P

rice

2.0

72.0

71.7

70.9

40.9

51.6

71.8

2.6

4*

7.4

6***

9.5

6***

10.2

3***

11.0

6***

10.8

0***

Mort

gSpre

ad

Lags

1.8

71.8

71.6

41.0

91.1

1.9

12.0

42.8

4*

4.9

7***

5.3

6***

4.6

7**

5.4

7***

4.8

4***

3.7

5**

Arm

Applicati

ons

1.1

81.1

81.1

90.7

0.7

11.3

11.3

32.0

63.7

7**

4.2

2**

3.7

2**

4.4

8**

4.0

7**

3.5

3**

6.6

8***

Fore

clo

sure

s–P

rim

e(d

BB

MD

PF

CL

)

None

3.8

8***

Aff

ord

3.7

5***

3.7

5***

Corp

Spre

ad

2.2

9*

2.2

9*

2.2

9*

∆B

BM

D30

2.3

0*

2.3

0*

2.3

0*

3.9

2***

∆B

BM

D90

1.9

5*

1.9

5*

1.9

8*

3.3

0***

3.4

3***

∆V

IX2.6

9**

2.6

9**

2.6

0**

2.8

7**

2.7

6**

3.4

5***

Em

plo

ym

ent

2.5

0**

2.5

0**

2.4

1**

2.5

3**

2.4

3**

2.9

8**

2.5

2**

House

Sta

rts

4.0

6***

4.0

6***

3.9

1***

3.6

8***

3.6

4***

4.2

6***

2.5

3**

2.9

1**

IND

PR

O3.9

5***

3.9

5***

3.8

0***

3.6

1***

3.5

4***

4.2

5***

2.6

2**

3.0

4**

3.3

6***

Reta

ilSale

s4.4

0***

4.4

0***

4.3

4***

3.9

1***

3.8

1***

4.4

8***

2.8

9**

3.2

3***

2.7

5**

2.7

6**

Tre

asS

pre

ad

4.6

2***

4.6

2***

4.3

7***

3.7

5***

3.7

1***

4.4

9***

3.0

6**

3.5

6***

2.7

7**

2.7

9**

3.6

6***

Uncert

ain

ty4.7

6***

4.7

6***

4.5

3***

3.7

5***

3.7

0***

4.4

9***

3.0

2**

3.5

0***

2.7

5**

2.7

6**

3.6

5***

3.9

1***

Loan-t

o-P

rice

8.9

1***

8.9

1***

8.6

6***

6.0

6***

6.1

4***

6.6

0***

5.0

0***

4.6

5***

3.7

3***

3.6

9***

4.3

4***

4.1

8***

4.2

8***

Mort

gSpre

ad

Lags

9.3

4***

9.3

4***

9.0

2***

7.9

0***

7.9

5***

8.0

7***

6.3

3***

5.6

3***

4.2

5***

4.2

5***

5.0

6***

5.3

0***

4.7

1***

3.9

5***

Arm

Applicati

ons

8.0

5***

8.0

5***

7.9

2***

6.9

6***

7.0

3***

6.8

0***

5.2

3***

4.9

1***

3.4

0***

3.5

3***

4.0

2***

4.4

5***

4.0

6***

3.3

2***

3.4

3***

Fore

clo

sure

s–Subpri

me

(dB

BM

DSF

CL

)

None

7.6

2***

Aff

ord

8.0

3***

8.0

3***

Corp

Spre

ad

8.8

9***

8.8

9***

6.3

2**

∆B

BM

D30

1.1

81.1

80.4

70.6

∆B

BM

D90

1.3

51.3

50.5

70.3

62.0

8∆

VIX

0.8

40.8

40.3

20.0

40.7

62.8

9*

Em

plo

ym

ent

1.0

61.0

60.4

30

0.0

70.7

23.0

1*

House

Sta

rts

0.4

0.4

0.0

70.5

20.2

30.0

20.8

61.6

4IN

DP

RO

1.3

51.3

50.4

80.0

20.0

50.8

12.6

63.7

7*

7.2

0***

Reta

ilSale

s1.7

71.7

70.7

50.4

10.8

51.9

36.4

7**

6.7

4**

7.0

7***

6.2

8**

Tre

asS

pre

ad

2.3

52.3

51.1

90.4

0.8

2.2

27.0

6***

7.0

9***

5.6

9**

4.8

8**

6.6

8**

Uncert

ain

ty1.0

91.0

90.6

60.2

30.5

41.3

52.5

22.3

52.4

42.9

3*

2.6

82.8

7*

Loan-t

o-P

rice

1.6

31.6

30.9

10.3

10.6

30.5

82.1

2.5

5.1

0**

6.2

7**

5.6

1**

5.6

7**

15.3

4***

Mort

gSpre

ad

Lags

4.1

0**

4.1

0**

2.1

62.2

2.8

2*

2.7

4.3

7**

4.4

2**

7.1

4***

7.8

3***

4.2

1**

4.6

6**

8.8

7***

4.4

9**

Arm

Applicati

ons

2.9

3*

2.9

3*

2.6

2.6

62.6

42.5

44.2

1**

4.2

7**

6.5

4**

7.3

6***

4.0

1**

4.4

5**

8.6

7***

4.4

6**

7.6

4***

65

Tab

le21

:M

onth

lyR

obust

nes

sC

hec

ks

usi

ng

BIC

∆C

antA

fford

None

0.1

8A

fford

0.2

40.2

4C

orp

Spre

ad

1.7

51.7

51.7

7∆

BB

MD

30

2.5

8**

2.5

8**

2.5

7**

2.8

8***

∆B

BM

D90

2.8

4***

2.8

4***

2.7

9**

2.7

3**

2.7

2**

∆V

IX2.7

8**

2.7

8**

2.7

4**

2.7

1**

2.7

1**

0.1

6E

mplo

ym

ent

2.5

6**

2.5

6**

2.4

9**

3.2

7***

3.2

7***

0.0

40.0

3H

ouse

Sta

rts

2.5

6**

2.5

6**

2.5

2**

2.9

7***

2.9

7***

0.0

10.0

10.0

5IN

DP

RO

2.6

2**

2.6

2**

2.5

6**

2.7

8**

2.8

0**

0.1

20.1

30.2

70.6

5R

eta

ilSale

s2.4

6**

2.4

6**

2.3

9**

2.5

4**

2.5

8**

0.9

11.0

31.5

82.9

2*

2.6

3T

reasS

pre

ad

1.8

2*

1.8

2*

1.6

21.8

2*

1.8

3*

0.3

60.5

40.7

80.9

21.1

70.1

Uncert

ain

ty1.7

41.7

41.5

21.7

51.7

60.3

0.4

30.7

40.9

61.2

30.0

50.1

3L

oan-t

o-P

rice

1.7

11.7

11.5

21.6

11.6

30.9

61.2

1.7

82.1

42.9

8*

0.2

50.8

90.6

7M

ort

gSpre

ad

Lags

1.7

11.7

11.5

1.6

1.6

11.2

41.3

31.9

82.4

42.8

4*

0.6

21.1

0.1

20.0

8A

rmA

pplicati

ons

1.2

41.2

41.0

91.0

81.0

81.8

31.8

42.5

33.1

4*

3.4

0*

11.4

0.1

80.1

20.1

4

∆U

ncert

ain

Futu

re

None

3.7

5*

Aff

ord

5.9

1**

5.9

1**

Corp

Spre

ad

3.3

3*

3.3

3*

2.4

4∆

BB

MD

30

2.6

52.6

52.8

6*

4.5

8**

∆B

BM

D90

2.6

72.6

72.9

0*

3.9

5**

3.9

8**

∆V

IX2.4

42.4

42.6

43.4

2*

3.4

4*

3.3

8*

Em

plo

ym

ent

0.6

50.6

51

2.1

82.1

72.3

12.5

6H

ouse

Sta

rts

0.5

70.5

70.9

91.3

41.3

81.3

21.4

63.7

2*

IND

PR

O0.0

60.0

60.0

20.1

90.2

30.2

60.1

0.0

30

Reta

ilSale

s0.0

70.0

70.0

10.1

50.1

80.2

50.1

60.0

80

3.4

4*

Tre

asS

pre

ad

0.1

0.1

0.0

30.1

80.2

0.2

80.1

70.0

10.0

23.4

4*

3.6

8*

Uncert

ain

ty0.2

30.2

30.1

80.5

10.5

50.6

20.4

60.0

20.0

33.3

8*

3.6

6*

3.8

3*

Loan-t

o-P

rice

0.3

10.3

10.2

30.7

40.7

61.1

0.9

80.0

90.1

53.1

0*

3.3

9*

3.5

3*

3.4

7*

Mort

gSpre

ad

Lags

0.3

40.3

40.2

51.1

61.2

1.7

91.6

51.4

31.2

50.0

90.1

10.2

31.0

51.0

7A

rmA

pplicati

ons

0.2

70.2

70.2

10.9

91.0

31.5

71.5

1.2

91.0

70.1

20.1

50.2

71.0

81.0

93.6

9*

Case

Shille

r

None

0.1

4A

fford

0.1

50.1

5C

orp

Spre

ad

0.2

80.2

80.2

2∆

BB

MD

30

0.7

50.7

50.6

10.0

6∆

BB

MD

90

0.7

40.7

40.6

10.1

40.3

3∆

VIX

0.3

30.3

30.2

20

0.0

60

Em

plo

ym

ent

0.0

10.0

10.0

40.5

20.3

50.3

90.1

3H

ouse

Sta

rts

0.9

60.9

60.6

70.2

20.1

20.2

80.0

70.0

4IN

DP

RO

1.7

1.7

1.2

80.6

30.7

30.2

80.6

30.5

71.6

2R

eta

ilSale

s1.6

11.6

11.1

90.5

60.6

20.2

20.3

70.2

91.1

40.0

1T

reasS

pre

ad

1.8

41.8

41.2

80.8

70.9

40.2

80.4

40.3

0.4

60.1

80.0

1U

ncert

ain

ty0.6

20.6

20.4

20.3

0.3

50.2

80.3

90.2

30.2

30.4

80.3

0.4

Loan-t

o-P

rice

0.2

90.2

90.1

70.1

30.1

70.1

40.2

0.1

80.1

70.6

50.4

50.5

90.0

2M

ort

gSpre

ad

Lags

0.3

60.3

60.3

20.0

80.1

20.1

10.1

50.0

90.0

90.7

10.5

80.5

60.2

30.1

1A

rmA

pplicati

ons

0.3

70.3

70.3

30.0

90.1

20.1

10.1

60.1

0.0

90.6

30.5

10.4

90.1

80.0

90.1

4

FH

FA

None

10.7

6***

Aff

ord

11.0

1***

11.0

1***

Corp

Spre

ad

10.1

9***

10.1

9***

10.0

1***

∆B

BM

D30

10.6

5***

10.6

5***

10.6

7***

11.1

0***

∆B

BM

D90

10.4

4***

10.4

4***

10.4

8***

11.1

3***

11.2

5***

∆V

IX12.4

3***

12.4

3***

12.5

2***

13.9

6***

13.8

4***

13.0

6***

Em

plo

ym

ent

11.1

5***

11.1

5***

11.2

7***

13.3

8***

13.0

9***

12.4

1***

10.1

1***

House

Sta

rts

11.7

2***

11.7

2***

11.8

3***

13.9

8***

13.8

2***

12.8

7***

9.9

1***

9.5

9***

IND

PR

O12.1

2***

12.1

2***

12.2

7***

14.4

8***

14.4

4***

13.5

4***

10.8

5***

10.4

5***

12.1

0***

Reta

ilSale

s13.4

4***

13.4

4***

13.5

7***

15.7

1***

15.6

1***

15.2

0***

13.7

9***

12.6

0***

14.5

0***

13.5

1***

Tre

asS

pre

ad

13.5

4***

13.5

4***

13.2

7***

14.9

2***

14.7

8***

14.0

5***

13.8

5***

11.6

2***

12.5

9***

12.0

4***

10.5

9***

Uncert

ain

ty14.3

9***

14.3

9***

13.9

6***

16.0

1***

15.8

9***

14.1

2***

13.7

4***

12.2

5***

13.3

3***

12.8

1***

11.4

5***

11.1

0***

Loan-t

o-P

rice

14.8

4***

14.8

4***

14.3

5***

16.0

9***

15.8

9***

13.8

9***

13.6

1***

12.6

0***

13.6

3***

12.8

8***

11.4

9***

11.0

6***

10.5

3***

Mort

gSpre

ad

Lags

12.6

8***

12.6

8***

12.2

7***

16.1

2***

15.9

4***

13.9

2***

13.6

0***

12.6

4***

13.8

3***

13.0

7***

11.5

7***

11.0

5***

10.1

6***

10.3

0***

Arm

Applicati

ons

10.3

8***

10.3

8***

10.2

9***

12.2

1***

12.1

1***

11.1

8***

11.6

5***

10.6

8***

11.4

4***

10.9

5***

9.7

0***

9.1

6***

8.5

3***

8.5

9***

8.9

9***

66

30

Days

Delinquent–

All

(dB

BM

D30)

None

0.1

3A

fford

0.1

10.1

1C

orp

Spre

ad

0.0

30.0

30

∆B

BM

D90

0.0

40.0

40.0

10.0

8∆

VIX

0.0

30.0

30

00.0

3E

mplo

ym

ent

0.2

20.2

20.3

60.5

90.5

70.7

7H

ouse

Sta

rts

0.1

50.1

50.3

20.5

0.4

80.6

60.0

1IN

DP

RO

0.3

10.3

10.5

50.6

30.6

20.8

10.1

10.5

1R

eta

ilSale

s0.3

10.3

10.5

40.5

80.4

70.5

30.0

70.2

40

Tre

asS

pre

ad

0.3

80.3

80.6

10.6

50.4

90.5

60.1

30.1

80.0

10.0

6U

ncert

ain

ty0.5

10.5

10.7

60.8

70.6

90.7

70.3

40.2

40.0

40.1

10.2

Loan-t

o-P

rice

0.4

10.4

10.6

30.7

20.5

40.5

70.1

50.1

30

0.0

40.1

70.2

8M

ort

gSpre

ad

Lags

0.5

30.5

30.7

0.7

50.5

60.6

0.1

40.1

30

0.0

30.1

60.2

60.1

2A

rmA

pplicati

ons

0.6

80.6

80.9

0.9

0.6

90.7

20.2

70.2

10.0

20.0

90.2

40.3

20.1

60.1

7

30

Days

Delinquent–

Pri

me

(dB

BM

DP

30)

None

1.6

2A

fford

1.5

81.5

8C

orp

Spre

ad

0.4

60.4

60.6

5∆

BB

MD

90

0.4

0.4

0.5

71.2

4∆

VIX

0.3

60.3

60.5

21.0

61.4

8E

mplo

ym

ent

0.5

30.5

30.7

0.6

80.7

30.8

House

Sta

rts

0.4

60.4

60.6

80.6

40.7

10.7

80.7

IND

PR

O0.5

40.5

40.8

10.7

30.8

0.8

70.7

91.6

6R

eta

ilSale

s0.2

0.2

0.3

20.3

90.3

50.3

70.3

50.7

10.5

2T

reasS

pre

ad

0.1

30.1

30.2

10.2

10.1

70.1

90.1

40.1

70.0

80.5

Uncert

ain

ty0.1

10.1

10.2

20.2

20.1

80.2

0.1

60.1

60.0

90.4

1.2

7L

oan-t

o-P

rice

0.0

20.0

20.0

80.0

80.0

40.0

40.0

60.0

70.0

20.3

1.3

21.6

4M

ort

gSpre

ad

Lags

0.0

30.0

30.1

0.0

80.0

40.0

40.0

60.0

70.0

20.2

61.2

81.5

91.6

Arm

Applicati

ons

0.0

70.0

70.1

70.1

50.1

0.1

0.1

30.1

40.0

60.3

91.4

71.7

21.7

21.7

5

30

Days

Delinquent–

Subpri

me

(dB

BM

DS30)

None

0.2

2A

fford

0.2

10.2

1C

orp

Spre

ad

0.9

70.9

70.8

5∆

BB

MD

90

0.7

80.7

80.6

60.0

7∆

VIX

0.6

30.6

30.5

20.0

10.1

1E

mplo

ym

ent

1.2

91.2

91.1

61.2

41.6

22

House

Sta

rts

1.5

91.5

91.4

21.4

21.7

52.0

61.5

5IN

DP

RO

1.7

71.7

71.5

41.5

1.8

22.1

41.7

20.8

2R

eta

ilSale

s1.6

71.6

71.4

31.3

71.5

71.6

41.3

0.4

50.0

4T

reasS

pre

ad

1.4

81.4

81.2

31.2

21.3

61.4

31.1

30.6

20.3

10.5

6U

ncert

ain

ty1.9

41.9

41.5

1.4

61.6

21.7

21.5

31.6

61.3

31.8

82.2

1L

oan-t

o-P

rice

1.2

91.2

90.8

80.8

50.8

40.8

61.0

41.0

90.7

81.1

51.2

60.1

8M

ort

gSpre

ad

Lags

1.3

81.3

80.8

90.8

30.8

20.8

41.0

21.0

30.7

61.0

51.1

90.1

90.1

6A

rmA

pplicati

ons

1.4

1.4

0.9

30.8

70.8

60.8

71.0

51.0

70.7

91.0

91.2

30.1

90.1

70.2

3

67

60

Days

Delinquent–

All

(dB

BM

D60)

None

2.0

3A

fford

2.0

52.0

5C

orp

Spre

ad

2.2

72.2

72.2

3∆

BB

MD

30

2.3

22.3

22.2

82.1

3∆

BB

MD

90

2.4

52.4

52.3

82.3

52.3

1∆

VIX

2.9

2*

2.9

2*

2.7

51.1

51.1

91.2

Em

plo

ym

ent

4.5

0**

4.5

0**

4.2

4**

3.8

2*

3.8

6*

3.8

6*

4.8

8**

House

Sta

rts

3.0

7*

3.0

7*

3.0

0*

2.3

12.1

42.2

22.8

6*

1.1

8IN

DP

RO

4.4

5**

4.4

5**

4.3

6**

3.1

5*

2.7

22.7

8*

3.5

2*

2.1

73.9

8**

Reta

ilSale

s4.2

7**

4.2

7**

4.0

5**

2.7

2.2

12.2

2.4

91.3

82.6

50.5

Tre

asS

pre

ad

3.1

5*

3.1

5*

2.6

91.6

61.2

21.2

1.5

70.8

91.5

80.6

91.3

5U

ncert

ain

ty3.4

0*

3.4

0*

2.7

7*

1.7

61.3

21.3

11.6

61.0

21.6

20.8

71.3

62.9

4*

Loan-t

o-P

rice

2.7

8*

2.7

8*

2.2

81.3

20.9

10.9

1.0

30.5

31.0

20.4

50.7

92.7

32.8

3*

Mort

gSpre

ad

Lags

2.3

42.3

41.8

91.3

40.9

10.8

81

0.5

0.9

10.2

20.3

42.7

7*

2.8

6*

2.0

6A

rmA

pplicati

ons

2.3

52.3

51.9

51.4

20.9

60.9

51.0

60.5

81.0

20.2

90.4

32.8

9*

2.8

3*

2.0

42.0

2

60

Days

Delinquent–

Pri

me

(dB

BM

DP

60)

None

2.5

8A

fford

2.9

5*

2.9

5*

Corp

Spre

ad

1.6

21.6

21.2

6∆

BB

MD

30

1.8

81.8

81.4

43.0

7*

∆B

BM

D90

1.9

11.9

11.4

42.9

5*

2.4

8∆

VIX

1.2

81.2

80.9

91.0

20.8

51.1

3E

mplo

ym

ent

3.0

4*

3.0

4*

2.5

12.4

31.9

42.0

23.1

8*

House

Sta

rts

1.7

71.7

71.4

91.4

61.0

31.1

21.6

90.6

1IN

DP

RO

1.7

21.7

21.4

51.4

11.0

11.1

21.6

90.6

62.4

5R

eta

ilSale

s1.3

1.3

1.0

61.0

80.7

50.7

20.8

0.1

80.7

50.7

4T

reasS

pre

ad

1.0

91.0

90.8

80.8

60.5

50.5

0.6

20.1

20.3

50.3

21.2

3U

ncert

ain

ty1.0

31.0

30.8

30.8

10.5

10.4

50.5

80.1

30.3

0.2

70.9

11.8

Loan-t

o-P

rice

11

0.8

30.8

20.5

30.4

20.3

30.0

20.1

0.1

0.5

21.4

82.7

9*

Mort

gSpre

ad

Lags

1.0

21.0

20.8

30.8

20.5

30.4

20.3

30.0

20.1

0.0

80.4

51.6

53.1

4*

3.0

2*

Arm

Applicati

ons

11

0.8

10.8

0.5

10.4

20.3

30.0

20.1

10.0

90.4

71.6

83.1

3*

3.0

0*

2.5

8

60

Days

Delinquent–

Subpri

me

(dB

BM

DS60)

None

0.3

9A

fford

0.3

70.3

7C

orp

Spre

ad

1.0

21.0

20.7

4∆

BB

MD

30

0.8

90.8

90.6

40.3

2∆

BB

MD

90

0.8

10.8

10.5

70.2

30.2

6∆

VIX

0.7

50.7

50.4

80.0

50.0

70.2

Em

plo

ym

ent

2.0

92.0

91.6

1.7

11.8

32.0

32.5

5H

ouse

Sta

rts

22

1.4

31.3

91.4

51.6

92.0

51

IND

PR

O1.9

71.9

71.3

81.2

91.3

31.5

51.9

41.0

80.7

9R

eta

ilSale

s1.5

91.5

91.0

70.9

91.0

11.0

71.0

40.4

70.1

70.0

1T

reasS

pre

ad

3.7

6*

3.7

6*

2.7

6*

2.8

5*

2.6

52.6

82.7

8*

2.3

32.5

52.8

4*

4.1

1**

Uncert

ain

ty6.4

3**

6.4

3**

4.0

2**

4.0

7**

3.8

4*

3.8

8*

3.9

2*

3.6

8*

4.0

2**

4.3

9**

5.5

2**

2.4

7L

oan-t

o-P

rice

3.1

8*

3.1

8*

1.7

81.8

11.6

61.7

31.6

12.1

82.5

32.9

7*

3.9

0*

1.6

20.5

9M

ort

gSpre

ad

Lags

3.1

1*

3.1

1*

1.8

31.9

71.8

51.9

31.8

32.3

82.7

12.9

3*

3.4

9*

1.4

20.4

20.2

3A

rmA

pplicati

ons

3.0

6*

3.0

6*

1.9

2.0

51.9

1.9

71.8

52.4

92.7

9*

3.0

1*

3.5

7*

1.5

70.4

30.2

50.4

68

90

Days

Delinquent–

All

(dB

BM

D90)

None

0.7

3A

fford

0.7

20.7

2C

orp

Spre

ad

0.1

20.1

20.1

1∆

BB

MD

30

0.1

20.1

20.1

0.7

6∆

VIX

0.0

80.0

80.0

70.8

70.8

4E

mplo

ym

ent

0.0

60.0

60.0

40.0

20.0

30.0

6H

ouse

Sta

rts

00

00

0.0

10.0

20

IND

PR

O0.0

10.0

10

0.0

20.0

30.0

50

0.1

6R

eta

ilSale

s0.2

10.2

10.2

10.3

10.3

20.4

50.1

50

0.0

6T

reasS

pre

ad

0.4

30.4

30.4

50.5

0.5

30.6

90.2

60.1

80.0

80.0

1U

ncert

ain

ty0.3

90.3

90.4

30.4

90.5

30.6

90.2

40.2

10.1

10

0.3

2L

oan-t

o-P

rice

1.8

31.8

31.9

72.0

92.1

2.2

50.5

40.4

90.2

90.0

60.1

40.7

Mort

gSpre

ad

Lags

2.3

42.3

42.3

82.5

22.5

22.6

70.7

70.8

20.7

40.3

80.0

20.2

40.2

4A

rmA

pplicati

ons

2.6

92.6

92.8

6*

3.0

4*

3.0

4*

3.1

6*

1.0

41.1

20.9

80.5

10.0

10.2

30.2

30.7

2

90

Days

Delinquent–

Pri

me

(dB

BM

DP

90)

None

3.2

1*

Aff

ord

5.4

2**

5.4

2**

Corp

Spre

ad

1.6

11.6

10.5

6∆

BB

MD

30

1.5

91.5

90.5

63.2

4*

∆V

IX1.8

1.8

0.7

83.7

6*

3.7

2*

Em

plo

ym

ent

2.0

82.0

81.4

31.4

1.3

31.0

2H

ouse

Sta

rts

3.0

7*

3.0

7*

2.0

31.5

61.5

1.2

21.1

9IN

DP

RO

1.8

91.8

91.0

40.6

20.6

30.5

50.3

20.4

7R

eta

ilSale

s1.3

31.3

30.5

80.2

80.2

70.1

80.0

80.1

21.9

7T

reasS

pre

ad

0.8

80.8

80.3

0.0

80.0

80.0

40.0

10

0.3

70.8

9U

ncert

ain

ty0.7

20.7

20.2

50.0

60.0

60.0

20

00.1

80.4

71.7

5L

oan-t

o-P

rice

00

0.0

70.3

20.3

20.3

60.1

10.1

60.0

40.1

61.2

13.0

4*

Mort

gSpre

ad

Lags

0.1

70.1

70.5

51.1

21.1

11.0

80.6

40.7

90.2

80.1

20.4

1.5

31.5

6A

rmA

pplicati

ons

0.0

60.0

60.5

61.1

31.1

11.0

80.6

50.7

90.2

90.1

10.4

31.6

1.6

33.2

9*

90

Days

Delinquent–

Subpri

me

(dB

BM

DS90)

None

0.9

2A

fford

0.8

70.8

7C

orp

Spre

ad

0.0

10.0

10

∆B

BM

D30

0.0

10.0

10

0.9

6∆

VIX

0.0

10.0

10.0

11

0.9

6E

mplo

ym

ent

0.0

10.0

10

0.0

10

0H

ouse

Sta

rts

0.0

80.0

80.0

40.0

30.0

30.0

20.2

1IN

DP

RO

0.0

90.0

90.0

40.0

20.0

20.0

20.1

60.4

Reta

ilSale

s0.0

10.0

10

00.0

10.0

10.0

30.0

80.1

9T

reasS

pre

ad

00

0.0

10.0

20.0

20.0

40

00

0.1

1U

ncert

ain

ty0.0

20.0

20

0.0

10.0

10.0

20.0

20.0

40.0

80.3

11.2

Loan-t

o-P

rice

0.3

0.3

0.5

30.5

50.5

80.6

20

00.0

10.1

20.8

30.9

1M

ort

gSpre

ad

Lags

0.4

50.4

50.6

90.6

60.6

80.7

30.0

10.0

10.0

10

0.5

20.3

40.3

4A

rmA

pplicati

ons

0.4

90.4

90.7

60.7

30.7

50.7

90.0

30.0

30.0

40

0.4

60.3

30.3

30.9

2

69

Fore

clo

sure

s–A

ll(d

BB

MD

FC

L)

None

2.3

1A

fford

2.1

42.1

4C

orp

Spre

ad

0.5

50.5

50.3

8∆

BB

MD

30

0.5

90.5

90.4

2.3

5∆

BB

MD

90

0.7

50.7

50.5

71.6

61.7

∆V

IX0.7

40.7

40.5

71.5

41.5

72.1

3E

mplo

ym

ent

0.0

90.0

90.0

30.0

20.0

20.0

10

House

Sta

rts

0.0

30.0

30.1

0.0

10.0

10.1

0.0

70.1

3IN

DP

RO

00

0.0

50

00

00

1.7

9R

eta

ilSale

s0.0

10.0

10

0.0

60.0

60.0

40.0

60.0

32.0

72.6

4T

reasS

pre

ad

0.0

40.0

40

0.0

60.0

60.0

40.0

60.0

40.5

0.4

60.2

8U

ncert

ain

ty0.0

70.0

70.0

10.0

80.0

80.0

60.0

80.0

70.1

70.1

20.0

40.8

2L

oan-t

o-P

rice

0.0

50.0

50.0

20.2

10.2

20.3

30.3

70.3

10.4

40.3

70.2

51.9

23.4

1*

Mort

gSpre

ad

Lags

0.0

10.0

10.0

70

00.1

30.1

90.2

40.4

50.3

70.2

51.9

13.4

7*

2.3

5A

rmA

pplicati

ons

00

0.1

60.0

10.0

10.0

50.0

80.0

80.1

80.1

40.0

71.3

63.0

6*

2.2

12.2

Fore

clo

sure

s–P

rim

e(d

BB

MD

PF

CL

)

None

2.8

1*

Aff

ord

2.7

12.7

1C

orp

Spre

ad

0.3

40.3

40.3

1∆

BB

MD

30

0.4

30.4

30.3

83.0

6*

∆B

BM

D90

0.4

90.4

90.4

32.3

82.2

5∆

VIX

0.4

10.4

10.3

51.7

1.6

12.1

5E

mplo

ym

ent

00

0.0

10.0

20.0

40.1

10.0

4H

ouse

Sta

rts

0.3

70.3

70.4

40.4

20.5

10.6

50.5

30.3

9IN

DP

RO

0.3

40.3

40.4

30.3

50.3

90.5

30.4

20.2

81.9

3R

eta

ilSale

s0.2

30.2

30.2

80.1

90.2

0.2

60.2

0.1

61.9

2.6

4T

reasS

pre

ad

0.2

0.2

0.2

40.2

20.2

30.2

80.2

10.1

60.3

0.3

60.2

6U

ncert

ain

ty0.1

0.1

0.1

70.1

50.1

60.2

0.1

50.0

80.0

70.0

80.0

30.9

1L

oan-t

o-P

rice

0.0

30.0

30.0

60.0

40.0

50.0

40.0

30.0

10.1

80.1

80.1

21.3

63.7

8*

Mort

gSpre

ad

Lags

00

0.0

30.0

20.0

30.0

20.0

20

0.2

20.2

40.1

91.6

44.4

6**

3.7

6*

Arm

Applicati

ons

0.0

10.0

10.0

60.0

50.0

70.0

60.0

60.0

40.0

70.0

80.0

61.2

43.9

7**

3.3

7*

2.5

4

Fore

clo

sure

s–Subpri

me

(dB

BM

DSF

CL

)

None

2.6

1A

fford

2.4

32.4

3C

orp

Spre

ad

1.5

81.5

81.2

∆B

BM

D30

1.3

51.3

51.0

12.1

8∆

BB

MD

90

0.7

80.7

80.5

0.3

30.3

8∆

VIX

0.8

0.8

0.5

30.3

70.4

22.8

7*

Em

plo

ym

ent

0.3

0.3

0.1

50.0

90.1

10.2

80.2

2H

ouse

Sta

rts

0.0

60.0

60.0

10.0

20.0

30.1

10.0

70.0

1IN

DP

RO

0.4

40.4

40.1

50.2

40.2

80.4

60.2

70.1

62.7

7*

Reta

ilSale

s0.6

50.6

50.3

40.5

20.5

50.7

80.7

10.4

13.3

3*

3.2

9*

Tre

asS

pre

ad

0.9

60.9

60.6

0.6

50.7

20.8

90.8

20.5

61.7

1.4

20.9

9U

ncert

ain

ty0.8

90.8

90.5

60.6

20.7

0.8

60.7

90.5

50.7

50.5

30.2

90.6

5L

oan-t

o-P

rice

0.6

60.6

60.3

80.4

90.5

60.4

0.3

70.1

70.4

40.2

70.1

30.4

82.6

5M

ort

gSpre

ad

Lags

1.0

21.0

20.5

10.7

40.8

20.6

10.5

70.2

70.5

60.4

20.2

70.7

33.6

1*

3.6

5*

Arm

Applicati

ons

0.9

10.9

10.4

30.6

20.7

20.5

20.4

90.2

0.3

80.2

70.1

50.5

83.5

0*

3.5

5*

2.5

3

70

G Appendix: OOS Robustness Checks (For Online Publication

Only)

Table 22: Daily One-Step-Ahead Out-Of-Sample Forecast Results using the BIC

Returns on Subprime Credit Default Swaps

Window Start End ABX AAA ABX AA ABX A ABX BBB ABX BBB-

30 2006-03-06 2012-02-29 5.44** 3.99* 4.78* 11.09*** 11.53***(0.03) (0.08) (0.09) (0.00) (0.00)

40 2006-03-20 2012-02-29 3.37** 3.61* 5.64** 6.02*** 6.81***(0.04) (0.06) (0.01) (0.00) (0.00)

60 2006-04-18 2012-02-29 1.55 3.08*** 2.81 6.25*** 5.97***(0.17) (0.01) (0.15) (0.00) (0.00)

80 2006-05-16 2012-02-29 1.71 1.89* 2.39* 4.76*** 4.63***(0.11) (0.07) (0.06) (0.00) (0.00)

100 2006-06-14 2012-02-29 1.27 0.46 0.59 3.89*** 3.26***(0.11) (0.58) (0.75) (0.00) (0.01)

120 2006-07-13 2012-02-29 1.73** 0.91 -0.27 3.37*** 2.79***(0.03) (0.23) (0.85) (0.00) (0.01)

140 2006-08-10 2012-02-29 0.71 0.37 -0.04 2.59** 2.96***(0.32) (0.62) (0.97) (0.02) (0.00)

Notes: See the notes for table 12.

71

Table 23: Daily One-Step-Ahead Out-Of-Sample Forecast Results using the AIC with theforecast window ending on 2009-12-31

Returns on Subprime Credit Default Swaps

Window Start End ABX AAA ABX AA ABX A ABX BBB ABX BBB-

30 2006-03-06 2009-12-31 3.90 4.00 3.53 12.37*** 14.41***(0.30) (0.22) (0.34) (0.00) (0.00)

40 2006-03-20 2009-12-31 2.23 2.49 4.67 8.94*** 8.18***(0.44) (0.36) (0.15) (0.00) (0.00)

60 2006-04-18 2009-12-31 1.20 3.29* 3.16 5.49** 4.98**(0.45) (0.07) (0.21) (0.02) (0.02)

80 2006-05-16 2009-12-31 1.33 4.15*** 2.52 4.30** 5.29**(0.31) (0.01) (0.27) (0.04) (0.02)

100 2006-06-14 2009-12-31 1.10 1.58 2.42 2.71 2.73(0.34) (0.22) (0.27) (0.13) (0.18)

120 2006-07-13 2009-12-31 1.85* 1.55 0.76 3.53* 3.70**(0.10) (0.23) (0.66) (0.06) (0.03)

140 2006-08-10 2009-12-31 1.17 1.31 0.17 2.58* 2.08(0.30) (0.26) (0.91) (0.08) (0.13)

Notes: See the notes for table 12.

Table 24: Daily One-Step-Ahead Out-Of-Sample Forecast Results using the BIC with theforecast window ending on 2009-12-31

Returns on Subprime Credit Default Swaps

Window Start End ABX AAA ABX AA ABX A ABX BBB ABX BBB-

30 2006-03-06 2009-12-31 4.48 2.53 2.71 10.71*** 11.04***(0.15) (0.34) (0.41) (0.00) (0.00)

40 2006-03-20 2009-12-31 2.39 2.65 4.69* 6.47** 6.15**(0.23) (0.23) (0.08) (0.02) (0.02)

60 2006-04-18 2009-12-31 0.44 2.95** 1.99 6.77*** 6.49***(0.73) (0.02) (0.39) (0.00) (0.00)

80 2006-05-16 2009-12-31 0.89 1.53 1.75 4.82** 4.24**(0.48) (0.19) (0.25) (0.01) (0.01)

100 2006-06-14 2009-12-31 0.59 0.05 0.10 3.43** 2.37(0.54) (0.95) (0.96) (0.03) (0.16)

120 2006-07-13 2009-12-31 1.29 0.79 -0.99 3.30** 2.14(0.18) (0.36) (0.57) (0.03) (0.12)

140 2006-08-10 2009-12-31 0.44 0.16 -0.59 2.02 2.40**(0.60) (0.85) (0.62) (0.17) (0.04)

Notes: See the notes for table 12.

72

Table 25: Weekly One-Step-Ahead Out-Of-Sample Forecast Results using the BIC

Housing Returns

(1) (2) (3) (4) (5)

Window Start End CS AL

30 2009-01-09 2012-10-19 8.59*** 21.66*(0.00) (0.09)

40 2009-03-20 2012-10-19 1.97 22.66(0.27) (0.16)

50 2009-05-29 2012-10-19 1.35 8.47(0.33) (0.19)

60 2009-08-07 2012-10-19 0.76 2.64*(0.53) (0.06)

70 2009-10-16 2012-10-19 0.94 1.98(0.41) (0.14)

Notes: See the notes for table 12.

73