loan contracting - information assymetry
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Loan contracting:Asymmetric information
Pranav Singh
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Information Asymmetry and FinancingArrangements: Evidence from Syndicated Lo
The Journal of Finance - April, 2007
AMIR SUFI
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What is the paper about?Effort to understand:
How information asymmetry affects financing arrangements, and
What financial institutions can do to reduce problems associated with in
asymmetry
Financing Arrangements studied: Syndicated Loans, because it is close t
representative financing arrangement.
Firms from all points of the credit spectrum - privately held, unrated, hig
investment grade - utilize Syndicated Loans
Syndicated lending represents 51% of U.S. corporate finance originated,
generates more underwriting revenue for the financial sector than both
debt underwriting
Information Asymmetry and Financing Arrangements: Evidence from Syndicated Loans, Amir Sufi
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Syndicated loan A loan whereby at least two lend
offer funds to a borrowing firm
Borrower/Firm The firm that takes the loan
Lead Arranger Bank /Financial Intermediary that
a relationship with the Borrower, negotiates terms
contract, and guarantees an amount for a price ran
Participants Lenders that fund part of the loan
Overview
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Theoretical BackgroundLead Arranger: Informed Lender; Participants: Uninformed Lenders
Moral Hazard: If Lead Arranger does not adequately benefit from the Lo
shirk her responsibilities of Due Diligence and Monitoring
Adverse Selection: If Lead Arranger has negative information about the
then she might not share the information with Participants and hold sm
the loan.
Lead Arranger holding a larger share of the loan, apart from the fear of
reputation in the long-run, mitigates both Moral Hazard and Adverse Se
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Key Takeaways Importance of Information Asymin Syndicate Formation
Information Asymmetry between Borrower and Participants: In case of Opaque borrowers, participant lenders are "closer" to the bor
both geographically and in terms of previous relationships
Information Asymmetry between Lead Arranger and Participants:
A previous relationship between the lead arranger and a potential partic
increases the probability that the potential participant funds the loan
Previous lead arranger-participant relationships are much less imp
(both in magnitude and statistical significance) than the previous
relationships between the borrowing firm and the participant lend
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DataDealscan matched with Compustat
Dealscan provides information on Lead Arrangers, Participants and Loan
Terms (usable dataset of 12,672 loans)
Compustat provides financial information for the borrowers (9,681 loans
25% of Loans have more than one tranche. There is one contract for thehence, the analysis is done at the level of Loans and not individual Tran
A subset of Top 100 Lead Arrangers and Top 125 Participants used to an
Participants are chosen as syndicate members.
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Descriptive StatisticsBorrowers have $1.8 billion in sales on average,and the median is $367 million. They have 1.12
previous syndications on an average and 31%
have an S&P senior unsecured debt rating.
Lead Arranger The average market share of
Lead Arranger is 9%.Loan The average loan is $364 million with a
maturity of 1,103 days. It has 8.1 lenders, 1.7 lead
arrangers, and 6.4 participant lenders. The lead
arranger keeps 28.5% of the loan on average.
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Measure of Information AsymmetryBased on the availability of :
SEC filings, and
third-party credit ratings
Private firms: firms with no ticker and no S&P senior unsecured credit r
Unrated firms: public borrowers with publicly available accounting data
an S&P senior unsecured debt rating
Transparent firms: public firms with S&P senior unsecured debt ratings
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Measure of Information Asymmetry SummaryTransparent firms are larger,obtain larger loans and have alarger number of lenders, leadarrangers, and participantlenders.
When borrowers are opaque,lead arrangers retain a largershare of the loan and form amore concentrated syndicate.
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Information Asymmetry and Syndicate StructureA. Syndicate Structure Regressions: how variation in the opacity of the b
firm affects syndicate structure
Syndi a +=1
12Yeardumt+ Xib+ Opaqueic+ ei
Syndi: measures of the syndicate, such as the number of lead arrangers, the nu
participants, and the percentage retained by the lead arranger
Opaque: the degree of Opacity Private, Unrated, Private and Unrated
c: how increased opacity affects syndicate structure
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Information Asymmetry and Syndicate Structure
Syndicate Structure Regressions (usingtransparent borrowing firms as the
omitted group)
Lead arrangers form the most
concentrated syndicates with the fewest
participants and retain the largest share of
the loan when borrowing firms are private.
The same pattern is observed, to a weaker
degree, when borrowing firms are public
but unrated.Why do Participants prefer higher m
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Information Asymmetry and Syndicate StructureB. Borrowing Firm Reputation
Opaque: Private, or Unrated, or Both
Column1: lead arrangers hold more of the
credit when the borrowing firm is opaque.
Column2: lead arranger holds less of the
credit when the borrower has more
previous syndicated loans
Column3: the lead arranger retains 1.85%
less of the loan when the opaque borrower
has more previous syndicated loans vs. a
new opaque borrower
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Information Asymmetry and Syndicate StructureC. Lead Bank Reputation
Proxy for reputation: Market share, byamount, in the previous year
Column 1: more reputable lead arrangersretain less of the loan
Column 2: the effect of reputation is morepronounced on loans to opaque firms
Only reputable borrowers with marketshares of over 0.32 = 13.36/4.25 (in the99th percentile) are able to retain no moreof the loan when the borrower is opaque.Econometrics 101: Do not extrapolateRegression towards the tails.
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Information Asymmetry and Syndicate StructureD. Moral Hazard versus Adverse Selection
Adverse Selection: Lead arranger would beforced to retain a larger fraction of the loanby the participants when a previous lendingrelationship is present while the Authorfinds opposite effect.
But, Previous lending relationship could bea result so many other factors like size,credit quality,
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Information Asymmetry and Syndicate StructureE. Effect of Collateral Collateral data available for less than half
of observations.
59% of loans are secured in usable dataset
Results are stronger in magnitude and
statistical significance among unsecured
loans
Problems of information asymmetry are
more severe among unsecured loans
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Information Asymmetry and Syndicate StructureE. Robustness TestObservation: Lead arrangers retain a larger share
of the loan and form a more concentrated
syndicate when the borrower more heavily uses
positive accruals or has high R&D investment,
interpreted as requirement of more intense
monitoring of the borrower (!).
What does (Accrual-Income)/Assets mean?
For ex.: Average Rate of Depreciation of Asset.
If the borrower is a new firm, or the future value
of its assets is going to decline fast, Participants
might want to hold less of such loan.
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Information Asymmetry and Participant ChoiceA. Characteristics of Participant LendersDataset: Top 125 participants and top
100 lead arrangers
Private on Unrated Borrower:
Participants are smaller and better
capitalized and likely to be in the same
geographical area as the borrowing firm.
Transparent Borrower: Participants are
more likely to have been a former lead or
former participant for the borrower.
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Information Asymmetry and Participant Choice
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Information Asymmetry and Participant B. Participant Choice Probit Analysis
How the characteristics of loan i and the characteristics of bank j influence the probab
j is chosen (!) as a participant on loan i.
Pr(Participant = Bankij) =f (a + b * Loani + y * Bankj + eij)
how c varies with the opacity of the borrowing firm
For new borrowers: When problems of information asymmetry are potentially severe
arrangers are more likely to choose participants that are geographically closer to the f
being in the same region as the borrowing firm increases the probability of being in
for transparent firms, 6.9% for private and 7.4% for unrated firms
being a foreign or unregulated domestic financial institution is negatively related to
as a participant
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Information Asymmetry and Participant ChoiceB. Participant Choice Probit Analysis
For repeat borrowers: there is a large amount of persistence in borrowiparticipant relationships.
a lender that is a former lead arranger for a borrowing firm is 6.2% mbe chosen as a participant, but the effect is more than 50% stronger wborrowing firm is private.
A lender that is a former participant for a firm is 25.5% more likely to as a participant, but the effect is 10% stronger if the borrowing firm isunrated.
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Information Asymmetry and Participant ChoiceHow do lead arranger-participant relationships affect participant choic
Being in the same region as the lead arranger and having been on a recent sy
the lead arranger both positively affect the probability of being chosen as a
Effects are smaller when compared with the effects of being a former lead o
for the borrowing firm
Effect of lead arranger-participant relationships does not vary by borrowing
When information asymmetry is severe, a lead arranger selects particip
on the participant's familiarity with the borrowing firm, not based on th
participant's familiarity with the lead arranger itself.
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Comments
Nicely written paper and relevant to real Finance each section starts with
question; Inference and economic significance of all tables explained proper
Omitted Variables? Opaqueness is highly correlated with Poor Credit Qual
Is it only information asymmetry or the interaction of information asymme
credit quality? Keeping credit quality of the Loan same, does closeness re
investments?
Syndications are of 2 Types Underwritten or On Best Efforts Basis
Opaque borrowers would have higher share of Best Efforts Syndication att
the successful attempts would be present in the dataset. Not easy to gene
findings about opaque borrowers.
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The Supply Side Determinants of Loan ContrStrictnessThe Journal of Finance - October, 2007
Justin Murfin
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What is the paper about?
Explores supply side of borrower lender nexus
Holding borrower risk fixed, how the recent default experienclender impacts the strictness of the equilibrium contract, and
what factors influence the resulting change in lender preferencontingent control.
There is substantial research on the influence of shocks to Lecredit availability not none on the strictness of terms of the
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Key Takeaways
3. Why borrowers accept stricter contracts and the resulting increase
intervention, when their own risk is unchanged?
Lack of outside options of accessing finance: borrowers who rely uplimited number of relationship banks and/or lack access to alternatiof cheap capital are exposed to considerable lender-induced contracprecisely because of their limited outside options.
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Data and Methodology
Data: Dealscan merged with Compustat from 1984 to 2008
Measure of Loan Contract Strictness:
Consider a single financial ratio r that receives a shock in the period after the lr = r + N(0, 2)
If a covenant for r is written such that r < r`` allocates control to the lender, th
p 1 ``` , represents the ex ante probability of lender control, wherestandard normal cumulative distribution function.
In multivariate setting: STRICTNESS p = 1 FN(r` r``), where FN is the multivcumulative distribution function with mean 0 and variance
Strictness includes both slack in the covenants (r`-r``) and covariance between
Parameters estimated using loan data in Dealscan and financial data of the firm
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Measure Validation
Average Contract Strictnessover time plotted against the
percentage of respondents
reporting tightening credit
standards in the Federal
Reserves survey of senior
loan officers
The survey leads the measure
of Strictness.
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Measure Validation
The measure Strictness is
significantly correlated withCovenant Violations even aftercontrolling for slack
Slack measured as r`-r``. Should it
be measured as``` instead?
Presumably stricter contracts arepredictive of violations
Why does Collateral increasecovenant violations?
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Summary Statistics
Loans for which there is information on
covenants vs. missing information oncovenants
Median Borrowing Firm is larger, has
lower credit risk, has higher probability
of having a credit rating, better credit
rating, higher maturity of loan, higher
arrangers and participants.
Lead Lender experiences an average
(median) of 1.51 (zero) defaults in the 90
days leading up to a loan contracting
date, which represents 0.1% of the total
number of loans outstanding
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Contract Strictness and Recent DefaultExperiencewhere iindexes borrowe
controls inXi,tattempt to capture observable proxies for borrower risk:
separate intercepts for each S&P long term credit rating,
AltmanZ-score to capture repayment risk for unrated firms and to allow for lagged responses to distress by rating agencies
borrower fixed effects
year dummies to control for Macroeconomic Risk regional or industry-specific risk
loan characteristics, such as security, maturity, deal amount, etc.
Default counts are demeaned by lender and standard errors are clustereborrower and lender dimensions.
Contract Strictness and
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Contract Strictness andRecent DefaultExperience WithBorrower Fixed Effects
Column I: Significant tighteningby banks in response to recentdefaults
Columns IIV: a short-lived effect
Banks are most sensitive to defaultsoccurring in the 90 daysimmediately prior to contracting.
No Control for the age of loans
when they defaulted.Loans with lower maturity havelower risks. So, a Loan thatdefaults early is potentially moretroublesome for bank than aloan that defaults much laterand would result in highercontract strictness by the bank.
Controlling for
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Controlling for
Macroeconomic
Risk
replace time dummies with the sumof total defaults in the economyover the matching 90-day period
If the lenders defaults are capturingunobservable macroeconomic risk,then aggregate defaults over thesame period will control for
Macroeconomic risk.Even after controlling forMacroeconomic Risk, Lendersrespond to their own defaultexperiences.
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Controlling forMacroeconomic Risk
replace time dummies with thesum of total defaults in theeconomy over the matching 90-day period
If the lenders defaults arecapturing unobservablemacroeconomic risk, thenaggregate defaults over the
same period will control forMacroeconomic risk.
Even after controlling forMacroeconomic Risk, Lendersrespond to their own defaultexperiences.
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Do Lender DefaultsProxy for Industry orRegion-Specific Risk?
Lender defaults may proxy forgeographic or industry-specific risk.
Both time dummies, and aggregatedefault counts are ineffectivecontrols.
Remove defaults that are related tothe current borrower by state, one-digit SIC code, or both.
Still a comparable significant effectof Default experience on ContractStrictness.
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Distinguishing CapitalEffects from OtherEffects
Is tightening is a result of bankcapital depletion mechanicallyassociated with borrower defaults?
Add controls for bank capitalizationratios and changes to bankcapitalization ratios around the timethe loans were granted.
Bank Capitalization in the currentperiod does affect contractstrictness
The coefficient of the DefaultExperience do not change much,indicating that the mechanicaleffect of bank capitalization isminor.
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Recent Defaults andScreening Ability
The effect of defaults on loansoriginated recently to the effect ofdefaults on loans originated in thedistant past (legacy loans)
Defaults to lender portfolios 90 dayssorted into bins based on the vintageand demeaned.
only defaults on the newest loansare significant at the 5% level.
No Control for Amount Outstanding!
Given the Deal Amount, older loanswould have amortized more. Bankwould have less exposure to them andwould care less about their Contract
Strictness.
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Effects of Defaults onReputationParticipants see the defaults of theLead Arranger and might demandstricter contracts to compensate forthe deteriorating reputation of LeadArranger.
If that is true, then the coefficienton defaults should be larger for
syndicated loans than for bilateralloans.
Interaction effect of Bilateral Loanswith Defaults has the opposite sign.
Reputation of Lead Arranger is notdriving Contract Strictness.
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Lender Effects andBorrower OutsideOptionsWhy do borrowers submit tostricter contracts when theirown risk profile is unchanged?
Separate borrowers into aboveand below median based onthe number of banks used overthe last four transactions.
Only borrowers with smallernumber of Lenders are affectedby the Default Experience ofLenders!
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Lender Effects andBorrower OutsideOptionsThe impact of availability ofnonbank sources of fundingfor the Borrower
Borrowers divided into CPissuers and Non-CP issuersbased on the credit rating
cutoff (A2)CP Issuers are not affected byLenders Default Experience!
No control for the number ofLenders!
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Comments
Paper is easy to understand, uses simple but innovative contr
It extends the influence of shocks to Lenders from supply of ccontract strictness.
But,
All Controls are carried out one by one. There should be a tab
the controls together.Not enough Lender Controls: What if some banks lent aggresswitnessed an increase in defaults and made contracts stricter
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Finally
A third desirable property of any strictness measure is scale. Setting sla
one implies a very strict cash flow covenant (a one-dollar reduction in cwill trigger default) but a current ratio covenant devoid of meaning (thecurrent assets to total assets can vary between 0.01 and 1.0 without evpage 1570
Oh My God!!!
Did the Author call Current Ratio = current assets/total assets, and JF
it?
Later the paper mentions that Current Ratio = current assets/current lia
And current ratio covenant of 1 is a properly scaled and a very useful c
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Asymmetric information effects on loan spre
Journal of Financial Economics - 2009
Victoria Ivashina
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What is the paper about?
Diversification Premium demanded by the Lead Bank in an S
Asymmetric information will cause participants to demand a large loan by the lead bank.
But, higher ownership by the lead would result in higher idiosyncratic riportfolio. Higher the Ownership, Higher would be the diversification coownership for the Lead Bank.
In equilibrium, the asymmetric information premium demanded by paroffset by the diversification premium demanded by the lead.
Rationale: the lead banks portfolio is not perfectly diversified and is unexposed to idiosyncratic risk. There is a risk component that will be pricelead bank but not by the participant banks. Why?
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Summary Statistics
Data from 1993 to 2004From CreditPro,Compustat and Dealscan
The Loans in Compustathave lower spread,higher facility amount
and belong to largerfirms.
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l k
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Empirical Framework
For each loan, Author calculates the change in credit risk resulting fro
addition of that loan to the lead banks loan portfolio Standard deviation of the loan portfolio default probability is used as
of credit risk
Default probability standard deviation = , where w is the loanweights(bank-specific), the probability of default covariance matrixspecific).
Computed at the two-digit Standard Industrial Classification (SIC) leveThe instrument the loans contribution to the credit risk of the lead bportfolio is the difference between the default probability standard dmeasured after and before the loan was added to the portfolio.
In place of Leads ownership, Author uses Leads median ownership for size quartile. Medians also have the same bias of higher ownership leve
E i i l F k
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Empirical Framework
A fitted value of the lead share, computed using the first-stage estimate
replace the observable lead share in the second stage.First Stage: Lead share =
1*Controls +
2*Instruments + ,
Second Stage: Required loan spread = 1*Lead bank shareF + 2*Contro
The loan amount and other non-pricing features of the loansuch as mcollateral, and covenantsare fixed before the syndication process andjustifies use of loan characteristics as control variables. Not entirely true
Controls for lender and borrower characteristics, and market conditionsBank fixed effects to control for reporting by select banks to Dealscan.
Approximately 2% of the deals in the regression sample have multiple leMuch Lower than reported by other Authors!
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First Stage RegressionLead share =
1*Controls +
2*Instruments +
Both instrumentsare jointly statisticallysignificant in explaining the share retained bythe lead bank.
Change in default probability standarddeviation negatively affects Leads share, and
Lending limit positively affects Leads share
Column 1: Dealscan Data;
Column 2: Matched Compustat Data withoutadding financial ratios;
Column 3: Matched Compustat Data withfinancial ratios
Higher Credit Ratings increase Leads Share!
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Second Stage ResultsRequired loan spread = 1*Leadbank shareF + 2*Controls +
the dependent variable is the loanspread and the focus is on thecoefficient on the lead share
IV approach corrects the bias.
OLS has positive coefficient, that is,
higher Lead Share increases theloan spread. Opposite of what Sufifound!
Higher Maturity lowers spread, andCollateral and Covenants increasespread. Opposite of what onewould expect!
R b t T t Di ifi ti Eff t
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Robustness Tests: Diversification Effect
Test of the diversification effect, that is, whether higher leads share results in highe
the lead demands to compensate for cost of diversification.Uses an instrument that exogenously shifts the level of asymmetric information within
syndicate without directly affecting the lead banks credit-risk exposure: Lead banks r
Lead banks reputation: is the maximum number of deals arranged by the same lead
same participants, measured over a three-year horizon and expressed as a percent of
underwritten during this period.
First Stage: For a given spread, when reputation is high, information asymmetry is lowbank would syndicate a larger fraction of the loan. Confirmed in the first stage results
previous table.
Second stage results show positive relation between the share retained by the lead ba
required spread. Due to the cost of diversification spreads increase with increase in
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Robustness Tests:Diversification Effect
Second Stage Results
positive relation between the shareretained by the lead bank and the requiredspread
Coefficient on Previous Lending Relationshipschanges sign in the second stage.
Now more Previous Lending Relationships
result in higher spread for a given Leads share.Is it due to the definition of the instrumentthat excludes other participants?
Maximum number of deals arranged by thesame lead bank with the same participants
Is this the right definition of reputation?
Robustness Tests: Alternative Credit Ris
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Management Techniques
Measure of Credit Risks: Standard deviation of the default probability of the leads loa
Banks use unobservable risk-management techniques, including credit derivatives swasecuritization through collateralized loan obligations (CLO), and loan sales on the seco
A banks actual credit-risk exposure might be difficult to measure
Alternative Credit Risk Management Techniques were not very important between 19
loan CDSs started trading in 2004
Total CLO volume for 19972001 is estimated to be around $100 billion, less than 2%amount of syndicated loans
less than 5% of the loans originated between 2000 and 2004 are quoted in the seco
market
Excluded all quoted loans from sample. Does that influence the OLS estimates?
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Robustness Tests:Upfront Fee
Is the upfront fee, rather than the spread,
being used to compensate the lead bankfor its credit-risk exposure?
In this case, the diversification premiumwould not affect the all-in spread drawn.
Anecdotal evidence suggests that there isnot much cross-sectional variability in theupfront fee, making it an unlikely channelfor settling a diversification premium.May not be entirely true!
Default probability standard deviation isimportant in explaining spread and notupfront fee.
The sample should be sliced intodifferent subcategories to assess the useof upfront fee as the compensating
mechanism
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Robustness Tests:Monitoring synergiesif industry concentration of the
loan portfolio is associated withsynergies in information collectionand monitoring, the spreaddemanded by the lead bank shouldbe lower
Look at the cases in which the leadbank does not have industry-
monitoring expertise.If monitoring creates synergy, thenthe coefficients should be higher,but they are not.
Coefficients are lower with low Tstatistics!
Comments
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Comments
Adds a systemic component of diversification effects to consider whileeffects of Information Asymmetry, but does not convince the author.
Like many other Finance papers, Author uses IV strategy but does notvalidity in detail.
T stats are very low, lower that 10% significance levels in some cases.
Coefficients are pointing in opposite directions in some regressions.
Assumption that monitoring effort of bank is independent of the dcost C to get
If C depends on , then the monotonous relation between C andshare, does not hold anymore.
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