Conditional Risk and Performance Evaluation: Volatility Timing, Overconditioning, and New Estimates of Momentum Alphas
By Oliver Boguth, Murray D. Carlson, Adlai J. Fisher, Mikhail Simutin. 2011
126 citations recorded by the source corpus.
Unconditional alphas are biased when conditional beta covaries with the market risk premium (market timing) or volatility (volatility timing). We demonstrate an additional bias (overconditioning) that can occur any time an empiricist estimates risk using information, such as a realized beta, that is not available to investors ex ante. Calibrating to U.S. equity returns, volatility timing and overconditioning can plausibly impact alphas more than market timing, which has been the focus of prior literature. To correct market- and volatility-timing biases without overconditioning, we show that incorporating realized betas into instrumental variables estimators is effective. Empirically, instrumentation reduces momentum alphas by 20–40%. Overconditioned alphas overstate performance by up to 2.5 times. We explain the sources of both the volatility-timing and overconditioning biases in momentum portfolios.
Subfields: Finance
View Paper: https://www.semanticscholar.org/paper/dc1166a7b34ec77c18e4f4895be33e7607aabf5a
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Citations in the corpus
- Momentum Crashes (2010, unknown): “Boguth, Carlson, Fisher, and Simutin (2010), building on the results of Ja-gannathan and Korajczyk (1986), note that the interpretation of the measures of abnormal performance ( i.e. , the alphas) in Chan (1988), Grundy and Martin (2001) and Rouwenhorst (1998) are problematic.”
- Time-Varying Risk Premium in Large Cross-Sectional Equity Data Sets (2016, background): “Shanken (1990), Cochrane (1996), Ferson and Schadt (1996), Ferson and Harvey (1991, 1999), Lettau and Ludvigson (2001), Petkova and Zhang (2005)).”
- A diagnostic criterion for approximate factor structure (2016, background): “va and Zhang (2005)). Time variation in risk biases time-invariant estimates of alphas and betas, and therefore asset pricing test conclusions (Jagannathan and Wang (1996), Lewellen and Nagel (2006), Boguth et al. (2011)). Ghysels (1998) discusses the pros and cons of modeling time-varying betas. A ”