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Visible and Hidden Risk Factors for Banks

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.

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Visible and Hidden Risk Factors for Banks

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  1. 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. 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. 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 1st 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. CAPM • Bank-Factor • Fama-French • Nine-Factor Market Models

  5. 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. 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 b = 1, but with variation around that mean (sb) • Use cross-sectional distribution of estimated parameters to make inference on “betas” in a given year t

  7. 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. Comparing Market Models: Results

  9. Adjusted R2: large banks

  10. Adjusted R2: other banks

  11. 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. R2 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. Average correlation of returns/residuals Large Banks Small Banks

  13. 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 1st PC accounting for only  27% of residual variance • But,  93% of loadings on 1st 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. 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. R2, Nine-Factor model: 46% vs. 28% • Residuals are typically diffuse • 1st PC: 27% vs. 21% • Residuals are relatively homogeneous and correlated • Factor loading on 1st PC: 93% vs. 84% • Mean pair-wise correlation of resids: 24% vs. 12%

  15. Average Adj. R2 across Sectors, 1997-2005

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

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