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...