Tail Risk and Asset Prices

Kelly, Bryan T.; Jiang, Hao

doi:10.3386/w19375

Cited by 77 publications

(112 citation statements)

References 26 publications

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“…A number of recent studies have suggested that aggregate U.S. stock market return is predictable over horizons ranging up to a few quarters based on the difference between optionsimplied and actual realized variation measures, or the so-called variance risk premium (see, e.g., Bollerslev, Tauchen, and Zhou, 2009;Drechsler and Yaron, 2011;Gabaix, 2011;Kelly, 2011;Zhou, 2010;Zhou and Zhu, 2009, among others). These findings of apparent predictability over relatively short quarterly horizons have potentially far reaching implications for many issues in asset pricing finance.…”

Section: Introductionmentioning

confidence: 99%

Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence

Bollerslev,

Marrone,

et al. 2011

SSRN Journal

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Recent empirical evidence suggests that the variance risk premium, or the difference between risk-neutral and statistical expectations of the future return variation, predicts aggregate stock market returns, with the predictability especially strong at the 2-4 month horizons. We provide extensive Monte Carlo simulation evidence that statistical finite sample biases in the overlapping return regressions underlying these findings can not "explain" this apparent predictability. Further corroborating the existing empirical evidence, we show that the patterns in the predictability across different return horizons estimated from country specific regressions for France, Germany, Japan, Switzerland and the U.K. are remarkably similar to the pattern previously documented for the U.S. Defining a "global" variance risk premium, we uncover even stronger predictability and almost identical crosscountry patterns through the use of panel regressions that effectively restrict the compensation for worldwide variance risk to be the same across countries. Our findings are broadly consistent with the implications from a stylized two-country general equilibrium model explicitly incorporating the effects of worldwide time-varying economic uncertainty.

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Section: Introductionmentioning

confidence: 99%

Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence

Bollerslev,

Marrone,

et al. 2011

SSRN Journal

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“…Starting with Rietz (), researchers have modeled the possibility of rare disasters, such as economic depressions or wars, to resolve the equity premium puzzle and related puzzles (e.g., Barro () and Gabaix (, )). Kelly () uses firm‐level stock price crashes every month to identify common fluctuations in tail risk across stocks and finds that past tail risk predicts future returns in the cross‐section. The disasters in the rare disasters literature are similar to the jumps we are interested in, but there are some differences: disasters are extremely rare and they do not match well the short‐dated options that we use in constructing the JUMP and VOL factors.…”

mentioning

confidence: 99%

Aggregate Jump and Volatility Risk in the Cross‐Section of Stock Returns

Cremers¹,

Halling²,

Weinbaum

2015

The Journal of Finance

254

154

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We examine the pricing of both aggregate jump and volatility risk in the cross-section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected returns. Consistent with theory, stocks with high sensitivities to jump and volatility risk have low expected returns. Both can be measured separately and are important economically, with a two-standard-deviation increase in jump (volatility) factor loadings associated with a 3.5% to 5.1% (2.7% to 2.9%) drop in expected annual stock returns.AGGREGATE STOCK MARKET volatility varies over time. This has important implications for asset prices in the cross-section and is the subject of much recent research (e.g., Ang et al. (2006)).1 There is also evidence that aggregate jump risk is time-varying. For example, Bates (1991) shows that out-of-themoney puts became unusually expensive during the year preceding the crash of October 1987. His analysis reveals significant time variation in the conditional expectations of jumps in aggregate stock market returns. Santa-Clara and Yan (2010) use option prices to calibrate a model in which both the volatility of the diffusion shocks and the intensity of the jumps are allowed to change over time. They likewise find substantial time variation in the jump intensity process, with aggregate implied jump probabilities ranging from 0% to over 99%. * Cremers is at Mendoza College of Business, University of Notre Dame. Halling is at Stockholm School of Economics, University of Utah. Weinbaum is at Whitman School of Management, Syracuse University. The authors thank Gurdip Bakshi, Turan Bali, Hank Bessembinder, Oleg Bondarenko, Nicole Branger, Fousseni Chabi-Yo (WFA discussant), Joseph Chen, Magnus Dahlquist, James Doran, Wayne Ferson, Fangjian Fu, Kris Jacobs, Chris Jones, Nikunj Kapadia, Christian Schlag, Grigory Vilkov (EFA discussant), Shu Yan, Yildiray Yildirim, Hao Zhou, and seminar participants at Boston University, ESMT Berlin, Imperial College London, Stockholm School of Economics, the 2013 IFSID and Bank of Canada conference on tail risk and derivatives, the 21st Annual Conference on Financial Economics and Accounting (CFEA) at the University of Maryland, the 12th Symposium on Finance, Banking and Insurance, the 2012 WFA Meetings, and the 2012 EFA Meetings for helpful comments and discussions. The authors are grateful to two anonymous referees, an anonymous Associate Editor, and Campbell Harvey, the Editor, for helpful suggestions that greatly improved the paper. The authors are responsible for any errors.1 Considerable research examines the time-series relation between aggregate stock market volatility and expected market returns. See, for example, Bali (2008), Campbell and Hentschel (1992), and Glosten, Jagannathan, and Runkle (1993). DOI: 10.1111/jofi.12220 578The Journal of Finance R While they examine the time-series relation between systematic jump risk and expected stock marke...

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“…A growing number of articles have used the market return variance, skewness, and kurtosis to predict and explain the cross-section of returns (Bali and Murray (2013), Bali, Cakici, and Whitelaw (2011), Chang, Christoffersen, and Jacobs (2013), and Amaya, Christoffersen, Jacobs, and Vasquez (2015)). Kelly and Jiang (2014) show that tail risk has strong predictive power for aggregate market returns and also explain the cross-section of returns. Bollerslev and Todorov (2011) and Bollerslev, Todorov, and Xu (2015) show how information extracted from options can be used to forecast future market return.…”

Section: Theoretical Motivation For Using Sdf Moments In Asset Pricingmentioning

confidence: 94%

Real-Time Distribution of Stochastic Discount Factors

Chabi-Yo

2018

SSRN Journal

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Tail Risk and Asset Prices

Cited by 77 publications

References 26 publications

Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence

Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence

Aggregate Jump and Volatility Risk in the Cross‐Section of Stock Returns

Real-Time Distribution of Stochastic Discount Factors

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