1. Filename Visible and Hidden Risk Factors for Banks Til Schuermann, Kevin J. Stiroh* Research, Federal Reserve Bank of New York FDIC-JFSR Bank Research Conference Arlington, VA 13-15 September, 2006 * Any views expressed represent those of the authors only and not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.

2. Filename 1 Banks and Systemic Risk  Are banks closely tied to the “observable risk factors”?  Are those residuals highly correlated?  Are banks more similar to each other than other sectors?  If “yes,” banks susceptible to systemic risk –DeBandt and Hartmann (2002): 2 channels Narrow contagion Broad simultaneous shock –Rajan (2005): compensation-induced herding

3. Filename 2 Overview  Estimate a range of standard market models and compare –Explanatory power –Residual correlations –Factor loadings  Principal component analysis (PCA) of residuals –Explanatory power of 1 st PC –Diffusion of hidden factors –Homogeneity of PC loadings  To provide context –Large vs. small banks –Large banks vs. large firms in other sectors

4. Filename 3 Market Models  CAPM  Fama-French  Nine-Factor  Bank-Factor

5. Filename 4 Data  Weekly bank equity returns, 1997 – 2005, year-by-year –On avg. 488 banks/year –CRSP –Conditioning variables from various data sources  Define “large” as inclusion in S&P 500 –About 34 large banks per year –About 454 small banks per year

6. Filename 5 Comparing Market Models  Need a way to compactly analyze  16,340 regressions (about 454  9  4 bank/year/model estimates)  Data is a panel, so one may think of each year as a random coefficient model (Swamy 1970) –Use mean group estimator (MGE) interpretation due to Pesaran and Smith (1995) –Firms may on average have  = 1, but with variation around that mean (   )  Use cross-sectional distribution of estimated parameters to make inference on “betas” in a given year t

7. Filename 6 Comparing Market Models: Results  Market factor dominates, followed by Fama-French factors –Rise in explanatory power from 1999-2002, but no obvious trend  Bank factors have relatively little impact –Change from empirical literature in the 1980’s (Flannery & James 1984) –Risk management / hedging  Other factors show considerable heterogeneity –Reflects differences in banks’ strategies and exposures

8. Filename 7 Comparing Market Models: Results

9. Filename 8 Adjusted R 2 : large banks

10. Filename 9 Adjusted R 2 : other banks

11. Filename 10 Relative to Large Banks, Small Banks Show…  Lower correlated returns –Mean pair-wise correlation of 11% vs. 57% (large)  Smaller link to systematic risk factors –Lower adj. R 2 of 13% vs. 46%  Stronger evidence of conditional independence –Mean pair-wise correlation of residuals of 3% vs. 25%  Less systematic market risk –  m of 0.5 vs. 1.2  Tighter link to interest rate and credit spread factors –Less intensive users of interest rate/credit derivatives  Stronger loadings on Fama-French factors

12. Filename 11 Average correlation of returns/residuals Large Banks Small Banks

13. Filename 12 Finding those Hidden Factors  Considerable residual variation remains for large banks –Mean pair-wise correlation of residuals  25%  Are hidden factors important? –Remaining variation is diffuse with 1 st PC accounting for only  27% of residual variance –But,  93% of loadings on 1 st PC have the same sign  Systemic implication –Given a shock to hidden factor, virtually all (big) banks will move the same way  Recent interest in credit risk –Frailty models of Das, Duffie, Kapadia & Saita (2006)

14. Filename 13 Are Banks Different?  Compare large banks to other large firms –10 other sectors comprised of S&P 500 firms  Return correlation is highest –57% vs. 36% (sector median)  Returns are relatively easy to explain –adj. R 2, Nine-Factor model: 46% vs. 28%  Residuals are typically diffuse –1 st PC: 27% vs. 21%  Residuals are relatively homogeneous and correlated –Factor loading on 1 st PC: 93% vs. 84% –Mean pair-wise correlation of resids: 24% vs. 12%

15. Filename 14 Average Adj. R 2 across Sectors, 1997-2005

16. Filename 15 Conclusions  Positive: no “special” risk factor for banks –Returns can be modeled conventionally –Residuals typically diffuse  Negative: residuals are relatively correlated and homogeneous –“Broad” systemic concern?

17. Filename 16 Thank You! http://nyfedeconomists.org/schuermann/