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Good Volatility, Bad Volatility, and the Cross Section of Stock Returns

Published online by Cambridge University Press:  30 January 2019

Tim Bollerslev Open the ORCID record for Tim Bollerslev [Opens in a new window] ,
Sophia Zhengzi Li  and
Bingzhi Zhao
Tim Bollerslev
Affiliation:
Bollerslev, boller@duke.edu, Department of Economics Duke University, the National Bureau of Economic Research (NBER) and Center for Research in Econometric Analysis of Time Series (CREATES)
Sophia Zhengzi Li*
Affiliation:
Li, zhengzi.li@business.rutgers.edu, Department of Finance and Economics Rutgers University
Bingzhi Zhao
Affiliation:
Zhao, bingzhizhao@gmail.com, Numeric Investors LLC
*
Li (corresponding author), zhengzi.li@business.rutgers.edu
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Abstract

Based on intraday data for a large cross section of individual stocks and newly developed econometric procedures, we decompose the realized variation for each of the stocks into separate so-called realized up and down semi-variance measures, or “good” and “bad” volatilities, associated with positive and negative high-frequency price increments, respectively. Sorting the individual stocks into portfolios based on their normalized good minus bad volatilities results in economically large and highly statistically significant differences in the subsequent portfolio returns. These differences remain significant after controlling for other firm characteristics and explanatory variables previously associated with the cross section of expected stock returns.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 55 , Issue 3 , May 2020 , pp. 751 - 781
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2019

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Footnotes

We thank an anonymous referee and Hendrik Bessembinder (the editor) for their very helpful comments, which greatly improved the paper. We also thank Peter Christoffersen, Alex Hsu, Anh Le, Jia Li, Andrew Patton, Riccardo Sabbatucci, Ravi Sastry, Gill Segal, and George Tauchen, along with seminar participants in the Duke financial econometrics lunch group, the Society for Financial Econometrics (SoFiE) summer school at Harvard, the 2017 Midwest Finance Association Annual Meeting, the 2017 Financial Intermediation Research Society Conference, the 2017 European Finance Association Conference, the 2017 Northern Finance Association Conference, and the 2018 Finance Down Under Conference for their many helpful comments and suggestions. The views expressed herein are solely those of the authors and do not necessarily represent those of Numeric Investors LLC nor Man Group PLC.

References

Adrian, T., and Rosenberg, J.. “Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk.” Journal of Finance, 63 (2008), 2997–3030.Google Scholar
Amaya, D.; Christoffersen, P.; Jacobs, K.; and Vasquez, A.. “Does Realized Skewness Predict the Cross-Section of Equity Returns?” Journal of Financial Economics, 118 (2016), 135–167.Google Scholar
Amihud, Y.“Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.” Journal of Financial Markets, 5 (2002), 31–56.Google Scholar
Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; and Labys, P.. “The Distribution of Realized Exchange Rate Volatility.” Journal of the American Statistical Association, 42 (2001), 42–55.Google Scholar
Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; and Labys, P.. “Modeling and Forecasting Realized Volatility.” Econometrica, 71 (2003), 579–625.Google Scholar
Ang, A.; Chen, J.; and Xing, Y.. “Downside Risk.” Review of Financial Studies, 19 (2006a), 1191–1239.Google Scholar
Ang, A.; Hodrick, R. J.; Xing, Y.; and Zhang, X.. “The Cross-Section of Volatility and Expected Returns.” Journal of Finance, 61 (2006b), 259–299.Google Scholar
Bali, T. G.; Cakici, N.; and Whitelaw, R.. “Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns.” Journal of Financial Economics, 99 (2011), 427–446.Google Scholar
Bansal, R.; Kiku, D.; Shaliastovich, I.; and Yaron, A.. “Volatility, the Macroeconomy, and Asset Prices.” Journal of Finance, 69 (2014), 2471–2511.Google Scholar
Barndorff-Nielsen, O.; Kinnebrock, S.; and Shephard, N.. “Measuring Downside Risk: Realised Semivariance.” In Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, Bollerslev, T., Russell, J., and Watson, M., eds. Oxford; New York: Oxford University Press (2010), 117–136.Google Scholar
Bernard, V., and Thomas, J.. “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research, 27 (1989), 1–36.Google Scholar
Bollerslev, T.; Li, S. Z.; and Todorov, V.. “Roughing up Beta: Continuous vs. Discontinuous Betas and the Cross-Section of Expected Stock Returns.” Journal of Financial Economics, 120 (2016), 464–490.Google Scholar
Bollerslev, T.; Tauchen, G.; and Zhou, H.. “Expected Stock Returns and Variance Risk Premia.” Review of Financial Studies, 22 (2009), 4463–4492.Google Scholar
Bollerslev, T., and Todorov, V.. “Estimation of Jump Tails.” Econometrica, 79 (2011a), 1727–1783.Google Scholar
Bollerslev, T., and Todorov, V.. “Tails, Fears, and Risk Premia.” Journal of Finance, 66 (2011b), 2165–2221.Google Scholar
Breckenfelder, H. J., and Tédongap, R.. “Asymmetry Matters: A High-Frequency Risk-Reward Trade-Off.” Working Paper, Stockholm School of Economics (2012).Google Scholar
Carhart, M.“On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 57–82.Google Scholar
Conrad, J.; Dittmar, R. F.; and Ghysels, E.. “Ex Ante Skewness and Expected Stock Returns.” Journal of Finance, 68 (2013), 85–124.Google Scholar
Cremers, M.; Halling, M.; and Weinbaum, D.. “Aggregate Jump and Volatility Risk in the Cross-Section of Stock Returns.” Journal of Finance, 70 (2015), 577–614.Google Scholar
Da, Z., and Schaumburg, E.. “The Pricing of Volatility Risk Across Asset Classes.” Working Paper, University of Notre Dame and Federal Reserve Bank of New York (2011).Google Scholar
Dittmar, R. F.“Nonlinear Pricing Kernels, Kurtosis Preferences, and Evidence from the Cross Section of Equity.” Journal of Finance, 57 (2002), 369–403.Google Scholar
Dreschler, I., and Yaron, A.. “What’s Vol Got to Do With It?” Review of Financial Studies, 24 (2011), 1–45.Google Scholar
Eraker, B.; Johannes, M.; and Polson, N.. “The Impact of Jumps in Volatility and Returns.” Journal of Finance, 58 (2003), 1269–1300.Google Scholar
Fama, E. F., and French, K. R.. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, 33 (1993), 3–56.Google Scholar
Fama, E. F., and MacBeth, J. D.. “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy, 3 (1973), 607–636.Google Scholar
Farago, A., and Tédongap, R.. “Downside Risks and the Cross-Section of Asset Returns.” Journal of Financial Economics, 129 (2018), 69–86.Google Scholar
Feunou, B.; Jahan-Parvar, M.; and Okou, C.. “Downside Variance Risk Premium.” Journal of Financial Econometrics, 16 (2018), 341–383.Google Scholar
Feunou, B.; Jahan-Parvar, M.; and Tedongap, R.. “Modeling Market Downside Volatility.” Review of Finance, 17 (2013), 443–481.Google Scholar
Feunou, B.; Jahan-Parvar, M.; and Tedongap, R.. “Which Parametric Model for Conditional Skewness?” European Journal of Finance, 22 (2016), 1237–1271.Google Scholar
Feunou, B.; Lopez Aliouchkin, R.; Tedongap, R.; and Xu, L.. “Variance Premium, Downside Risk, and Expected Stock Returns.” Working Paper, Bank of Canada, Syracuse University and ESSEC Business School (2017).Google Scholar
Feunou, B., and Okou, C.. “Good Volatility, Bad Volatility, and Option Pricing.” Journal of Financial and Quantitative Analysis, 54 (2019), 695–727.Google Scholar
Hansen, P., and Lunde, A.. “Realized Variance and Market Microstructure Noise.” Journal of Business and Economic Statistics, 24 (2006), 127–161.Google Scholar
Harvey, C. R.; Liu, Y.; and Zhu, H.. “… And the Cross-Section of Expected Returns.” Review of Financial Studies, 29 (2016), 5–68.Google Scholar
Harvey, C. R., and Siddique, A.. “Conditional Skewness in Asset Pricing Tests.” Journal of Finance, 55 (2000), 1263–1295.Google Scholar
Hasbrouck, J.“Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data.” Journal of Finance, 64 (2009), 1445–1477.Google Scholar
Herskovic, B.; Kelly, B.; Lustig, H.; and Van Nieuwerburgh, S.. “The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications.” Journal of Financial Economics, 119 (2016), 249–283.Google Scholar
Hou, K., and Loh, R. K.. “Have We Solved the Idiosyncratic Volatility Puzzle?” Journal of Financial Economics, 121 (2016), 167–194.Google Scholar
Jegadeesh, N.“Evidence of Predictable Behavior of Security Returns.” Journal of Finance, 45 (1990), 881–898.Google Scholar
Jegadeesh, N., and Titman, S.. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48 (1993), 65–92.Google Scholar
Jiang, G., and Yao, T.. “Stock Price Jumps and Cross-Sectional Return Predictability.” Journal of Financial and Quantitative Analysis, 48 (2013), 1519–1544.Google Scholar
Koike, Y., and Liu, Z.. “Asymptotic Properties of the Realized Skewness and Related Statistics.” Annals of the Institute of Statistical Mathematics, (2018), 1–39.Google Scholar
Lee, S.“Jumps and Information Flow in Financial Markets.” Review of Financial Studies, 25 (2012), 439–479.Google Scholar
Lehmann, B. N.“Fads, Martingales, and Market Efficiency.” Quarterly Journal of Economics, (1990), 1–28.Google Scholar
Lo, A. W., and MacKinlay, A. C.. “When Are Contrarian Profits Due to Stock Market Overreaction?” Review of Financial Studies, 3 (1990), 175–205.Google Scholar
Nagel, S.“Evaporating Liquidity.” Review of Financial Studies, 25 (2012), 2005–2039.Google Scholar
Newey, W. K., and West, K. D.. “A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55 (1987), 703–708.Google Scholar
Savor, P., and Wilson, M.. “Asset Pricing: A Tale of Two Days.” Journal of Financial Economics, 113 (2014), 171–201.Google Scholar
Shleifer, A., and Vishny, R. W.. “The Limits of Arbitrage.” Journal of Finance, 52 (1997), 35–55.Google Scholar
Stambaugh, R. F.; Yu, J.; and Yuan, Y.. “Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle.” Journal of Finance, 70 (2015), 1903–1948.Google Scholar
Tulchinsky, I.Finding Alphas: A Quantitative Approach to Building Trading Strategies. New York, NY: Wiley (2015).Google Scholar
Yan, S.“Jump Risk, Stock Returns, and Slope of Implied Volatility Smile.” Journal of Financial Economics, 99 (2011), 216–233.Google Scholar
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