Publications
Conditional risk and the pricing kernel (with David Schreindorfer) (Journal, SSRN, online appendix, code)
Journal of Financial Economics, September 2025, 171(9)
SIX Best Paper Award, Annual Conference of the Swiss Society for Financial Market Research, 2023
We propose a methodology for jointly estimating the pricing kernel and conditional physical return densities from option prices. Pricing kernel estimates show that negative stock market returns are significantly more painful to investors in low-volatility periods. Density estimates reflect a significantly positive risk–return trade-off, and provide new evidence on equity premium bounds, variance premium’s predictive power for returns as well as the co-movement between higher return moments.
Presentations (including coauthor presentations): Arizona State, Stockholm School of Economics, McGill, Princeton, Carnegie Mellon University, Goethe University, University of Oregon, University of Amsterdam, ITAM, Michigan State, University of Georgia, University of Iowa, University of Oregon, University of Virginia, Vanderbilt University, BMRG Conference on Macro and Financial Economics, Virtual Derivatives Workshop, EFA, CICF, SoFIE annual conference, BI-SHoF Conference, Junior European Finance Seminar, FMA Conference on Derivatives and Volatility, MFA, SGF, AFA
Working Papers
Betting on Stocks with Options? [New version] (with Adrien d'Avernas, Christian Schlag, Martin Waibel, and Chunjie Wang)
We examine whether expected stock returns translate into expected option returns as predicted by standard theory. They do not. Using machine-learning estimates of expected stock returns, we uncover a pronounced U-shaped relation between expected returns and volatility. Both high and low expected stock returns therefore coincide with elevated volatility, which increases option prices and largely offsets the expected payoff differential. We derive a model-free lower bound on expected option return spreads and show it is strongly violated in the data. As a result, equity options are an inefficient instrument for harvesting stock risk premia. A calibrated Black--Scholes model reproduces these empirical patterns.
Presentations: Virtual Derivatives Workshop 2025, CFF Finance Conference, St.~Gallen Financial Economics Workshop, ESSFM in Gerzensee, BI-SHoF Conference, Conference on Deep Learning for Dynamic Stochastic Models, FinEML Conference
A Nonlinear Market Model from Options (Online Appendix)
A nonlinear market model, estimated from S&P 500 index options, prices 76 of 80 cross-sectional stock return anomalies. Its pricing performance can be attributed to conditioning on market volatility, incorporating higher-order moments beyond variance, and capturing a sizable upside risk premium. The largest improvements occur for prominent anomalies such as momentum, betting against beta, idiosyncratic volatility, and liquidity, which exhibit strong higher-moment exposure. By recovering the pricing kernel directly from options–enabling precise measurement of higher-moment risk premia–the model reveals that nonlinear market pricing is a quantitatively important and previously underappreciated driver of cross-sectional return variation.
Presentations: WFA, SFS Cavalcade NA, European Winter Finance Summit, ESSFM in Gerzensee, SGF, SED, JEF Conference, DGF, Swedish Conference in Economics, NFN, Asset Pricing Conference by LTI (Turin), FMA/CBOE Derivatives and Volatility, Erasmus Rotterdam, Arizona State, Göteborg, Princeton
Volatility Risk Exposure and the Cross-Section of Expected Option Returns (with Christian Schlag) (Online Appendix) R&R
We show that in the presence of a variance risk premium, delta-hedged at-the-money returns decline with the underlying's volatility, whereas the opposite holds for out-of-the-money options. For unhedged returns, the variance-risk channel can counteract the equity-risk-premium channel and dominates it for out-of-the-money contracts. These predictions overturn several well-known results and are strongly confirmed empirically. Moreover, our results help distinguish among competing explanations for the cross-section of option returns, including variance-risk compensation, constrained intermediaries, and lottery-preference views.
Presentations: Liverpool Workshop in Option Markets, Virtual Derivatives Workshop, FMA/CBOE Derivatives and Volatility, NFN Young Scholars Finance Webinar Series, Carnegie Mellon University, University of Alabama
The Pricing Kernel is U-shaped (Online Appendix)
Based on a novel GARCH model with structural breaks, I show that the pricing kernel is consistently U-shaped. The main driver of the change in results is a correction of otherwise biased return volatility forecast of standard GARCH models.
Presentations: AFA, TADC, NFA, SGF, AFFI, Carnegie Mellon University, Collegio Carlo Alberto, Goethe University, Nova School of Business and Economics, Stockholm School of Economics, University of Toronto, University of Zurich, Warwick University
Work in Progress
Machine Learning Option Returns, joint with Adrien d'Avernas, Christian Schlag, Martin Waibel and Chunjie Wang
The Pricing of Higher Moment Risk in Stock Returns – New Evidence, joint with Caio Almeida and Gustavo Freire