[1] Option-Implied Risk Premia with Intertemporal Hedging (with Elise Gourier and Hugues Langlois), This draft, 8/16/2025. (Previously titled: Equity Risk Premium with Intertemporal Hedging)
Abstract: The equity and variance risk premia at a horizon T1 depend on the risk of changes in the future economic environment beyond T1. We derive novel estimates of these risk premia that account for intertemporal risk hedging and embed information on the market’s term structure of risk. Crucially, our risk premia can be measured ex ante using option prices. We find that intertemporal hedging accounts for up to 80% of the equity and variance risk premia. In particular, intertemporal hedging increases the equity risk premium and decreases the variance risk premium in times of market expansion, characterized by long investors’ horizons. Our estimates improve the out-of-sample R2 of market return prediction by a factor of up to 2.
Eurofidai-ESSEC, Paris December 18, 2025.
2025 NFA, Calgary, Alberta, Canada, September 19-21, 2025.
2025 Canadian Derivatives Institute Conference, September 11-12, 2025.
2025 World Congress of the Econometric Society, Seoul, South Korea, August 18-22, 2025
Virtual Derivative Workshop on March 18, 2025
2023 MFA Conference in Chicago, IL, on March 16-18, 2023
China International Conference in Finance in CICF, July 2022
[2] Maxing Out Entropy: A Conditioning Approach (with Jintao Du and Yan Liu). This draft, 7/24/2025
Abstract: We develop a systematic approach to bounding entropy by incorporating conditioning information. Our bounds feature a fixed-point solution to a dynamic asset-allocation problem, interpretable as generalized “Sharpe ratios" in the entropy space—our bounds balance exploiting physical return predictability and hedging risk-neutral higher-order moments. Applying our approach to various return predictors, we document enhanced entropy restrictions that more than double the benchmark equity risk premium. When incorporating higher-order return moments, our bounds are sharper than the corresponding optimally scaled Hansen-Jagannathan bounds over short horizons. We highlight our results’ implications in diagnosing leading macro-finance models and their consistency across different data.
SFS Cavalcade North America 2022, UNC
Midwest Finance Association (MFA), Chicago, March 10-12, 2022
SoFIE Conference, UCSD, San Diego, June 2021
[3] Distorting Arrow-Debreu Securities: New Entropy Restrictions Implied by the Option Cross Section (with Jintao Du and Yan Liu ) This draft, 06/2025
China International Conference in Finance in CICF, July 2021
European Finance Association, August 2020
7th Asset Pricing Workshop, Frankfurt , Germany, September 2020
[4] A Factor Model for Stock Returns Based on Option Prices (with Turan Bali and Scott Murray), This draft, 05/2022
Abstract: Option prices reflect investors' assessment of future risk and risk premia, and therefore contain information about expected stock returns. We show theoretically that expected stock returns are a function of the difference between risk-neutral and physical variance, and the stock borrow fee. Based on this theory, we construct an empirical factor model that includes factors formed by sorting stocks on option-based variables. We find that the model has a higher tangent portfolio Sharpe ratio than extant factor models and outperforms such models at explaining the performance of portfolios formed by sorting on many option-based and traditional asset pricing variables.
[5] What Is the Conditional Autocorrelation on the Stock Market?
[6] The Real-Time Distribution of Stochastic Discount Factors , This draft, 08/2018
Presented at the Canadian Derivatives Institute (CDI) 2018 (formerly IFSID)
Latest draft: 07/2015 (with Gurdip Bakshi and Xiaohui Gao).