Download Document

How the Subprime Crisis went
global: Evidence from bank
credit default swap spreads
Barry Eichengreen, Ashoka Mody, Milan Nedeljkovic, Lucio
Sarno, 2012
Presenters:
Jaroslav Mida
Zuzana Rakovská
Vendula Bečvaříková
Alicia Berrios
March 17, 2015
Contents
• Introduction
• Arguments
• Implications
• Common factors in CDS spreads
•
•
•
•
Setting
Preliminary data analysis
Dynamic factor
Results
• Additional spillovers
• Correlating latent factors with observed financial variables
•
•
•
•
•
•
Considerations
Correlates
The real economy
Emergence of financial factors
After Lehman
Sensitivity analysis
• Conclusion
INTRODUCTION
Introduction
Problem with securities backed
by sub-prime mortgages
accounting for only some 3% of
U.S. financial assets
*subprime mortgages  “second chance lending”
Could infect the entire U.S.
and global banking systems.
Arguments
• The authors highlight that although the banking system was
generally affected, it was the banks’ fortunes that differed
substantially (in terms of market assessment).
• i.e. differentials in the impact on their share prices
• The authors also argue that it wasn’t necessarily one
institution that triggered the crisis, but the deteriorating
global economic and financial conditions that undermined the
banks as a class.
Arguments
• To prove this alternative argument they analyze:
1.
2.
Risk premium on debt owed (analyzing individual banks’ CDS
spreads) by 45 of the largest financial institutions.
The “common factors” underlying weekly variations in the CDS
spreads of individual banks via PCA analysis.
*Credit
Default Swap (CDS) credit derivative contract in which the buyer receives credit protection,
whereas the seller of the swap guarantees the credit worthiness of the debt security.
Source: www.investopedia.com
Spreads for
different banks
move
independently.
Spreads move
together.
Common Factors
Bank-Specific Factors
Variations in CDS Spreads
Implications
• The share of common factors was high (62%) pre-crisis, and
even higher (77%) during the crisis.
• Banks as a “class” their fortunes rose and fell together
during these normal times, and during the outbreak.
1.
2.
Awareness that credit risk is increasing
Post Lehman Bros and increased interdependency and
counterparty risk
3. Spillovers of US banks  European Banks
4. Global recession became imminent and catalyzed further
deterioration of banks’ loan portfolios
Ultimately infecting the entire global financial system!
COMMON FACTORS IN CDS SPREADS
Setting
• N = 45 global banks (banks as a common term for financial
institutions including incśurance companies)
• Period: July 29, 2002 to November 28, 2008
• Data: weekly spreads (risk premiums denominated in basis
points – 1 basis point = 0,01%) of 5-year CDS
• end-of-day quotes from the New York market for payment in US
dollars on US dollar-denominated national amounts averaged over
the week
• Data from Bloomberg
• => 331 observations per bank
Preliminary Data Analysis
Preliminary Data Analysis
• means vary across banks significantly (from 17 to 101 b.p.)
• St. Dev. close to or larger than means (sovereign CDS spreads
typically lower)
• Max/Min difference across banks => time-series variation
again
Time-Variation in Median
Spreads
Time Variation in Median Spreads
• In 2001 – events of September 11 => CDS spreads elevated
(some paid more than 100 b.p. for protection)
• In 2002 tech. bubble, CDS spreads begin to decline => lowest
in the week of January 17, 2007 (median spread of full
sample 7,5 b.p.)
• After => dramatic increase with twin peaks
• Bear Stearns rescue
• Lehman Brothers failure
Dynamic Factor Model of CDS
Spread
• Question: „Did movements in spreads reflect common drivers?“
• To answer=> estimation of unobserved factors generating common
movements using Dynamic Factor Model
𝑋𝑖,𝑡 = Λ𝑖,ℎ 𝐹ℎ,𝑡 + Φ𝑖 𝐿 𝑋𝑖,𝑡−1 + 𝜀𝑖,𝑡
where:
𝑋𝑖,𝑡 - weekly changes in CDS spreads (i – banks, t – week)
Λ𝑖,ℎ - factor loadings
𝐹ℎ,𝑡 - unobserved factors (h=1,…,k)
𝜀𝑖,𝑡 - cross-sectionally and time correlated,
heteroskedastic
How to get unobserved factors and
their loadings?
• Method=> Principal Component Analysis (PCA) – linear
transformation of variables into set of artificial ones
• Principal Components – set of most important ones (the highest
variance)
• “the covariance among the series can be captured by a few
unobserved common factors”
• Model is estimated recursively after first 150 data points to assess
time-variation
Some Caveats
• Primary interest – common drivers across the whole set of banks
analyzed (time variation in conditional pairwise correlations across
the CDS analyzed is not modeled)
• DFM used here does not allow for time-varying volatility
• Possible bias in the estimation of the PC and subsequent estimation of the
correlation between the PC and observable economic variables
• For robustness, DFM with time-varying factor volatility also estimated
(details in next sections)
Results
• Four factors used to explain time-variation of
the CDS spreads=> PCA showed that perceived
riskiness of different international banks
moved together to a considerable extent
• Note that this does not tell whether these common factors reflected interconnections within the
banking system or were the result of common external factors!
• The distinction will be explored in the next sections
• Period till 2007 – 60% of movements in CDS spreads explained
• Period till early 2008 - increase in co-movements (in the end 70% explained)
• Period of Lehman failure – 80% reached and then slight moderation
• Is commonality higher or lower? – compared to
results of Longstaff et al. (2010) {first factor
explains 32-48%, four factors: 60-70%} => YES
Results
ADDTIONAL SPILLOVERS
Additional spillovers
- Evaluate, whether, after considering common factors, the CDS spread of a
particular bank is influenced by current/lagged values of CDS spreads of other
banks, i.e. whether there is significant information in CDS spread over and
above that contained in common factors
- To test, regress the change in CDS spread of a bank on its own past values,
common factors and current/lagged changes in CDS spread of another bank +
additional dummy for sub-prime period.
- Estimated for all pairs of banks, leading to 2025 regressions and test
significance of λj using heteroskedascity-robust LM specification.
- In only about 2.5% of regressions the coefficient was significant at 5%
significance level.
- Additional spillovers reached their peak around Lehmann Brothers crisis.
- Banks affected by spillovers: ING, Royal Bank of Scotland, Bank of America, J.P.
Morgan etc.
- Moreover, assess the international spillover from U.S. banks to Europe and vice
versa. Before sub-prime period, little evidence (US->Europe), later on large
spillovers.
- Europe->US – moderate evidence, interestingly, later on during sub-prime
period, less evidence of spillovers.
CORRELATING LATENT FACTORS WITH
OBSERVED FINANCIAL VARIABLES
Correlating latent factors with
observed financial variables.
- Goal: examine relation between factor and observed fin. variables by
measuring the association between them and investigating under which
conditions correlations with factors are informative.
- To evaluate whether any of candidate series yields the same information as
factors, we can use following criteria:
- Where
,
. and
is the sample variance. The
idea behind this test is to assess whether any of the candidate series can be
represented as a linear combination of latent factors. This statistics is bounded
between 1 and 0, equaling 1 if there is exact association and vice versa.
- We can also examine the relation with a subset of factors. To determine the
number of factors, one can use moment selection criteria, but there must be
independence between idiosyncratic part of movement in CDS spreads and
observed series. Otherwise, the results would be biased.
Correlating latent factors with
observed financial variables.
- One thing to note is that it is hard to determine a threshold that would signal
“matching” between factor space and individual observed series, when there is
contamination by some degree of noise.
- Another concern is whether correlations uniquely identify relationships, e.g.
correlation of 0.5 between first factor and series can really reflect the
correlation, but can also be spuriously picking up a correlation between second
factor and series.
- This is a direct consequence of PCA method and non-consistency of individual
PC estimates – first PC can be a good approximation of first factor, but it also
can be a linear combination of all factors.
Correlates
- First set of variables representing real economy includes corporate default
risk measured by high-yield spread (HYS), risk aversion (VIX) and returns
on S&P 500.
- Second set representing banks’ financial risks includes credit spread (LIBOR
minus overnight index swap), liquidity spread (overnight index swap minus
Treasury bill yield) and spreads on asset-backed commercial paper (ABCP)
- First GMM-based model and moment selection criterion (to check validity
of correlates) are performed.
- None of the proposed series is correlated with the idiosyncratic part of CDS
spreads since the frequency of rejections of the null among all
randomizations of the data is very small for all samples -> we can use the
moment selection criteria to evaluate the relationship between factors
and observed series.
- The results from full and subsample estimation of the criteria suggest that
the information in the set of observed series can be associated with the
three or four factor subspace.
Real economy prior to Subprime Crisis
- Three correlates: High-yield spreads (spreads on bonds issued by less-thaninvestment-grade issuers) reflect increased corporate default probabilities and are
known to do well in predicting short-term GDP growth. The S&P 500 average
reflects the market’s perception of the economic outlook, while the VIX is
a measure of economic volatility embedded in stock price movements.
Association between factors and series
Association between factors and series –
Explanation
- Perception of banks’ risks was shaped by a global factor that can be summarized by
corporate default risk.
- This is reasonable as HYS is a good predictor of economic prospects (its movements
capture the operation of the financial accelerator: high spreads -> high expected
default rates, lower credit supply, reduced growth prospects -> high spreads.
- In addition, HYS is a significant explanatory variable of emerging market spread
differences across countries and their movements over time.
- Stock returns contain both up/downside movements, but banks’ risk are more defined
by downside risks as showed by HYS.
- VIX is significantly less correlated with factors than HYS, which implies that a higher
generalized risk aversion might not translate into banks’ risk premia.
Association between first and
second factor and series
First factor reflects global perceptions of downside risks, while the second gives
more weight to general movements in expected future profitability. Note,
though, that the second factor explains a much smaller fraction of the overall
variance of CDS spreads. Hence returns had a much weaker association with
spreads’ movements.
The Emergence of financial
factors
• With the onset of the crisis and through the Bear Stearns bailout,
the association with the HYS declined
• An initial sharp increase in association with VIX died down to
pre-crisis levels by the time of
the Bear Stearns rescue
• Fears about the stability of the
banking system were driven
more by problems in the banks‘
positions in securities than by
ability of their corporate
customers to stay current on
their loans
The Emergence of financial
factors
• Once the Subprime Crisis started, the relevance of financial
variables (mainly related to banks‘ credit and funding risk)
acquired greater prominence
• A common metric of banks‘ credit risks is TED spred, the
difference between the interest rates on inter-bank loans and
short-term US government debt – this captures the risk
premium on bank borrowing
• We can define TED spread as well as TED=(LIBOR-OIS)+(OIS“Tbill“), where OIS stands for „overnight index swap“
• Thus TED spread reflects not only a banking-sector credit risk
(the LIBOR-OIS differential), but also includes liquidity or
flight-to-quality risk (the OIS-Tbill differential)
The Emergence of financial
factors
The TED spread rose sharply in the post-Lehman crisis period. While
the liquidity premium also increased, the more substantial increase
was in credit risk
The Emergence of financial
factors
• Association of common factors with these
„new“ financial variables rose following the
start of the crisis → perceived bank risk
stemmed now more from banks‘ own
internal credit and funding risks (previously
stemmed from the development of real
economy)
• Credit risk has the largest R² of these three
variables.
• While the change in the pattern of
relationships clearly points to greater
emphasis on the internal workings of the
banking system, the estimates found even at
their elevated levels during this phase are
small
• Not much changed between the Bear
Stearns rescue and the Lehman failure.
After Lehman
• Unprecedented alignment of risks
• The increase of the association between the CDS factor space
and all of the observed variables
• The increase in credit and funding risk premia reflected the
stress faced by banks.
• By the R² criterion, we can see an increase in the association
between the space of common factors in bank CDS spreads
and all three correlates
Sensitivity analysis
• The consistency of the PC factor space estimates constitutes the
basis for the empirical analysis. Consistency is obtained under
several assumptions.
• To assess the seriousness of these limitations, authors used three
additional methods of estimation: extention of specification by
Makarov and Papanikolaou (allows for time-varying factor
volatility), weighted PC estimation, robust PCA estimation
• The results from all of them genereally support the main findings
from the PCA discussed earlier.
• In particular, allowing for time-varying volatility in the factors, the R²
estimates are virtually identical to the previous ones; WPC
estimation confirm interpretation of factors (both quantitatively
andqualitatively); dynamics of correlations obtained from robust PC
remains unchanged
CONCLUSION
Conclusion
• How did the crisis spread from the subprime segment of the
U.S. financial market to the entire U.S. and global financial
system?
Important common factor  “banks as a group”
1. Associated as a proxy for the banking-sector credit risk
premium, especially prior to the Bear Stearns rescue.
2. Association with the state of the real economy (previously evident
before the crisis) was reduced.
1. Investors were actually not yet concerned with the prospect of
a global recession in which would impact the bank’s loan
portfolios as with other credit risks affecting the banks.
3. Transfer of importance of the common factor once relating to
credit risk to funding risk.
1. Realization of the deepening recession, or the real economy
asserting itself.
1.
THANK YOU FOR ATTENTION