measuring systemic risk in the southeast asian financial system

Measuring Systemic Risk in the Southeast Asian Financial System David T. Hamilton, PhD, Managing Director Samuel W. Malone, PhD, Director Originally presented as part of a Moody’s Analytics webinar | June 2015

2 Measuring Systemic Risk in the Southeast Asian Financial System

Speaker Biographies

David T. Hamilton

David T. Hamilton is a Managing Director, Head of Stress Testing and Credit Risk Analytics for the Asia Pacific Region, based in Singapore. Dr. Hamilton has been with Moody’s for eighteen years and has held various senior positions in both Moody’s Investors Service and Moody’s Analytics. Over his career with Moody’s, Dr. Hamilton has done research on various aspects of credit risk in a variety of sectors, including corporate, sovereign, municipal, and structured finance. Prior to joining Moody’s in 1997, Dr. Hamilton worked in the Regional Economics group at the Federal Reserve Bank of Philadelphia. Dr. Hamilton has lectured on credit risk topics at prestigious universities around the world, including Columbia Business School and The International Center for Financial Asset Management and Engineering (FAME) in Lausanne, Switzerland. Dr. Hamilton is on the editorial board of the Journal of Credit Risk. He holds a B.A. in economics and classical studies from Texas A&M University and a Ph.D. in financial economics from the City University of New York.

Contact: +65 6511 4650 tel [email protected] Moody's Analytics 6 Shenton Way OUE Downtown 2 #14-08 Singapore 068809


Speaker Biographies

Samuel W. Malone

Sam Malone is Director of Economic Research at Moody's Analytics. Dr. Malone has taught at and consulted for top institutions in Europe and the Americas, including Oxford, the University of Navarra, the European Commission, the Central Banks of Venezuela and Peru, and several large North American financial institutions. He is coauthor of the book Macrofinancial Risk Analysis, published in the Wiley Finance series with foreword by Nobel Laureate Robert Merton, as well as the author of numerous academic journal articles in economics and statistics. His articles have been published in outlets such as World Development, the Journal of Applied Econometrics, the Journal of Financial Econometrics, the International Journal of Forecasting, the Annual Review of Financial Economics, the Journal of Investment Management, and GARP Risk Intelligence. He holds undergraduate degrees in mathematics and economics from Duke University, where he studied as an A.B. Duke scholar and graduated with summa cum laude Latin honors, and masters and PhD degrees in economics from the University of Oxford, where he studied as a Rhodes scholar.

Contact: +1 610 235 5204 tel [email protected] Moody's Analytics 121 North Walnut Street, Suite 500 West Chester, PA 19380 USA


Moody's Analytics operates independently of the credit ratings activities of Moody's Investors Service. We do not comment on credit ratings or potential rating changes, and no opinion or analysis you hear during this presentation can be assumed to reflect those of the ratings agency.


Systemic Risk: A Key (and Often Missing) Part of Stress Testing 1


Moody’s Analytics’ Research on Systemic Risk

» Today’s webinar is based on a publicly available research paper available on moodysanalytics.com

» The paper is part of a series of research papers on systemic risk

» MA has also studied the US and Australian financial systems, as well as global banks

» Although important in its own right, understanding systemic risk is a crucial component of stress testing


A Complete Bank and Financial System Stress Tests Needs to Consider Systemic Risk

Thailand

Malaysia

Indonesia

Korea

Philippines

Singapore

Indonesia Korea Thailand Malaysia

Real Exchange Rate

-63% -33% -27% -23%

Nominal Interest Rate

32% 12% 8% 3.5%

GDP -13.7% -5.8% -9.4% -6.7%

Equity Market (USD)

-50% -46% -58% -79%

The 1997-1998 Asian Financial Crisis Exposed Financial System Fragilities

Source: Andrew Berg, “The Asian Crisis: Causes, Policy Responses, Outcomes,” IMF Working Paper, WP/99/138, 1999.

Impact of the Asian Financial Crisis


Defining Systemic Risk

» We define systemic risk as the potential for a shock, endogenous or exogenous to the financial system, to cause broad-based financial system failure while inflicting collateral damage on other economic sectors

» This “domino effect” points to operationally useful ways of measuring systemic risk – Systemic risk is defined by the strength of the dependencies and linkages among

financial institutions; i.e. their interconnectedness

– Systemic risk has a time dimension: the failure of an systemically important firm precedes the failure of other firms; i.e. systemic failures are caused by the failure of one or more SIFIs

» Interconnectedness has traditionally been gauged using various measures of size, such as the size of nonbank deposits, the size of domestic interbank borrowing, and the importance to the domestic payments system

» Too-interconnected-to-fail is potentially more important than too-big-to-fail


Measuring Systemic Risk 2


Step 1. We Measure Credit Risk Using Default Probabilities

A Stylized Company Balance Sheet

Assets

Cash

Operating assets

Fixed assets

Loans

Intellectual property

Brand value

Government support

Equity

Residual claim after all liabilities paid off

Liabilities

Short-term debt

Long-term debt

Operating liabilities

Expected future distribution of asset value

Probability of default

Expected Default Frequencies, or EDFs, Reflect an Underlying Causal Model of Default Risk that Incorporates Investors’ Forward-Looking Views


Step 2. We Measure Interconnectedness Using Granger Causality » Granger causality is statistical causality: a change in one time series is useful

for predicting changes in another time series

» Econometric tests for Granger causality are performed for pairs of firms – Series of regressions are run that include lagged values of a firm’s own history (firm A)

and the history of another firm (firm B)

– If the lags of firm B are statistically significant, we say that “B Granger-causes A”

– If sign of the first lag coefficient in the regression is positive, then a rise in B’s EDF is “forcing”; if the coefficient is negative, then a rise in B’s EDF has a “damping” effect on A at the monthly frequency

» Granger causality cannot tell one why causality exists, just that it does, and the direction and strength of the causality

» The Degree of Granger Causality (DGC) is the ratio of the total number of active connections in the Granger causality network to the total number of potential connections (ranges between 0 and 1)

» DGC is a summary measure of the strength of interconnectedness

12 12

0.0

0.5

1.0

1.5

2.0

2.5

1 201 401 601 801 1001 1201 1401 1601 1801 2001

Firm B Firm A

Stylized Example: Firm B Granger-Causes Firm A

Source: Moody’s Analytics

PD %

Time


Step 3. Research Study Design

» The results of our empirical analysis are based on a dataset of financial institutions (SIC code between 6,000 and 6,799) domiciled in the ASEAN-5 group of countries: Indonesia, Malaysia, the Philippines, Singapore and Thailand

» We limit our dataset to financial institutions with at least US$1 billion in book assets observed at some point over their available histories

» Our study spans the period from 1995 to October 2014, and includes 201 unique financial institutions: 36 in Indonesia, 49 in Malaysia, 30 in the Philippines, 46 in Singapore, and 40 in Thailand

» We use Expected Default Frequency (EDF) Measures, which are one-year default probabilities, to measure the spillover effects of credit risk across firms


Model Results 3


Shock Propagation Shows Little Correlation with Firm Size

Out EDF Book Assets

Country Value Quantile Value (%) Quantile Value (USD mil) Quantile Financial Institution

TMB Bank Public Co. Limited THA 0.306 1 0.28 0.34 24,568 0.8

OSK Holdings Berhad MYS 0.281 0.99 0.09 0.03 880 0.07

Bank of the Philippine Islands PHL 0.264 0.98 0.39 0.59 28,868 0.81 UOB-Kay Hian Holdings Limited SGP 0.256 0.98 0.30 0.4 2,079 0.38 CIMB Thai Bank Public Co. Limited THA 0.248 0.97 0.33 0.49 7,798 0.61

CitySpring Infrastructure Trust SGP 0.24 0.96 0.11 0.09 1,513 0.24 Bangkok Land Public Co. Limited THA 0.24 0.95 0.08 0.02 1,697 0.3

Hong Leong Capital Berhad MYS 0.231 0.94 0.34 0.51 951 0.08

Bangkok Life Assurance PCL THA 0.231 0.93 0.34 0.52 6,259 0.56 Bangkok Bank Public Co. Limited THA 0.231 0.93 0.24 0.23 78,431 0.95

Firms with the Highest Out Measure as of October 2014 The Out measure indicates that TMB Bank’s EDF movements Granger-cause EDF movements in 30.6% of the other financial institutions in the network


EDF Measures Gauge the Likelihood of Default

» The risk of default reached a historic peak during the Asian financial crisis

» The GFC, as severe as it was in the West, is a relatively minor blip in the time series for the ASEAN-5 countries

» These results suggest a potentially useful and powerful way of monitoring the likelihood of systemic crises

0

2

4

6

8

10

12

14

16

95 97 99 01 03 05 07 09 11 13

Size-weighted EDF

Systemic influence-weighted EDF

EDF %

Sources: Moody’s CreditEdge, Moody’s Analytics

Weighted Average PD Measures Over, 1995-2014


Systemic Risk Over Three Distinct Crises

0.05

0.10

0.15

0.20

0.25

0.30

0.35

95 97 99 01 03 05 07 09 11 13

» Systemic risk and the high risk of contagion that characterized the Asian financial crisis is captured by the peak 0.31 DGC measure

» It took at least four years for systemic risk to subside to levels that prevailed before the Asian financial crisis

» Credit risk spillovers arising from the global financial crisis were a short-lived event for the ASEAN-5 group of financial institutions


Asian Financial Crisis

Dot-Com Bust

Global Financial Crisis

Degree of Granger Causality in ASEAN-5 Financial Network, 1995-2014


Leverage was One of the Key Causes of the Asian Financial Crisis

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

95 97 99 01 03 05 07 09 11 13

Size-weighted leverage

Systemic influence-weighted leverage


Weighted Average Leverage Measures, 1995-2014

» Size-weighted leverage is nearly always higher than systemic influence-weighted leverage: larger financial institutions tend to lever up more

» A second and more important implication is that a firm’s size is not perfectly correlated with the spillover dimension of systemic risk contribution


Highly Connected Firms are More Volatile than Large Firms

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

95 97 99 01 03 05 07 09 11 13

Size-weighted volatility

Systemic influence-weightedvolatility


Weighted Average Volatility Measures, 1995-2014 Volatility (% annualized) » Unlike average EDF levels

and leverage values, the weighted volatility measures rise throughout but peak well after the Asian financial crisis

» Systemic influence weighted volatility is everywhere above size-weighted volatility: Firms that exhibit a relatively high Out ratio, and therefore have a high potential for contagion, also exhibit higher asset volatility


Riskier Financial Institutions Increasingly Drive the System During Crises

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

95 97 99 01 03 05 07 09 11 13

EDFOut.spearman

LeverageVol.spearman


Weighted Average Volatility Measures, 1995-2014 Spearman Correlation » EDF levels and systemic

influence correlations tend to be negative during calm periods and positive during crisis periods

» Leverage and volatility correlations are always negative

» During times of crisis, the EDF-Out correlations increase, as do the leverage-volatility correlations

» EDF-Out correlation tends to spike at the beginnings of crisis episodes


Directed Network Graph, October 2014

» The sets of lines connecting financial institutions in Thailand, Singapore and Malaysia are numerous, giving the graph a very dense appearance on the right side

» The lines connecting Thailand, Singapore and Malaysia also tend to be green, meaning that the relationship between financial institutions in these countries is positive: An increase in credit risk among financial institutions in one of these countries has a high propensity to cause an increase in credit risk in the others


Summary and Conclusion 4


Know Yourself, Know Your Neighbors

» We described a useful measure of systemic risk using a network approach that – Is straightforward to implement, with intuitive outputs that are easily interpretable

– Can leverage existing stress testing resources or requires minimal incremental investment

» These tools can be of indispensable use to both financial institutions and regulators for estimating the current and future level of systemic risk and for identifying the sources of its changes

» Managers of banks and financial institutions can assess their counterparty risks more fully via consideration of joint and conditional default likelihoods calculated using network-based simulations

» Stress testing requirements across the globe are evolving and converging, and we expect that greater emphasis will be placed on measuring and understanding systemic risk going forward – As has been the case so far, financial institutions will bear the responsibility of

producing stress test estimates that incorporate a view about systemic risk


Let Moody’s Analytics Help

If you are interested in understanding the systemic risk of a particular portfolio, country, or region, contact us and we will perform a basic analysis similar to that we discussed

in this presentation


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