Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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