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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 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.
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
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
CAPM • Bank-Factor • Fama-French • Nine-Factor Market Models
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
Comparing Market Models • Need a way to compactly analyze 16,340 regressions (about 45494 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
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
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
Average correlation of returns/residuals Large Banks Small Banks
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)
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%
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?
Thank You! http://nyfedeconomists.org/schuermann/