Publications

Published and Accepted Papers

[18]    An Intertemporal Risk Factor Model forthcoming, Management Science. May 08, 2024 (with Andrei Gonçalves and Johnathan Loudis)

Abstract:  Current factor models do not identify risks that matter to investors. To address this issue, we provide a factor model implementation of the ICAPM, which captures market risk and intertemporal risk (i.e., changes in long-term expected returns and volatility). We build our intertemporal risk factors as mimicking portfolios for changes in dividend yield and realized volatility and demonstrate that, ex-post, they capture news to long-term expected returns and volatility. Our estimated risk price signs are in line with the ICAPM and their magnitudes imply an average risk aversion around five. Moreover, the ICAPM performs comparably with (and mostly better than) previous factor models in terms of its maximum (out-of-sample and cost-adjusted) Sharpe ratio as well as its pricing of the testing assets Lewellen, Nagel, and Shanken (2010) recommend: single stocks, industry portfolios, correlation-clustered portfolios, and bond portfolios. 

[17]  A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models  forthcoming, Management Science. November 22, 2023 (with Johnathan Loudis) .

Abstract: We develop a methodology to decompose the conditional market risk premium and risk premia on arbitrary moments of excess market returns into components related to contingent claims on down, up, and normal market returns. We call these components the downside, upside, and central risk premia. The decomposition does not depend on assumptions about investor preferences or the market return distribution, and can be computed in real time using a cross-section of option prices. The components' contributions to total risk premia vary significantly over time and across investment horizon. Our risk premium decomposition offers a powerful tool for evaluating representative agent models in a conditional setting. We develop a related methodology to estimate analogous conditional decompositions implied by leading representative agent models, and compare these to data-implied decompositions. Although many representative agent models match the unconditional market risk premium, they generally do a poor job matching the downside, central, and upside risk premia both conditionally and unconditionally. 

[16] Never a Dull Moment: Entropy Risk in Commodity Markets  forthcoming, Review of Asset Pricing Studies,  February 2023 (with Hitesh Doshi and Virgilio Zurita) 

Abstract:  We develop a new approach to determine investors’ risk compensations for all distributional moments of a security. Using the concept of entropy, a summary of all moments of a risky security, we derive the relationship between expected returns and their compensation for entropy risk. Entropy risk premium (ERP), entropy under the physical minus the risk-neutral measure, indicates the hedging cost against changes in risks associated with all moments of the return’s distribution. Applying our model to the commodity markets we …nd that ERP carries economically significant information for the cross-section of returns that is different from individual or combined moments.

[15]  Multivariate Crash Risk,  Journal of Financial Economics, Volume 145, Issue 1, July 2022, Pages 129-153  (with Markus Huggenberger and Florian Weigert) 

Abstract: This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH and we empirically confirm that stocks with high MCRASH earn significantly higher future returns than stocks with low MCRASH. The premium is not explained by linear factor exposures, alternative downside risk measures or stock characteristics. Extending market-based definitions of crash risk to other well-established factors helps to determine the cross-section of expected stock returns without further expanding the factor zoo .

[14]  Generalized Bounds on the Conditional Expected Excess Return on Individual Stocks  Management Science, March 2022  (with Chukwuma Dim  and Grigory Vilkov).

Abstract:  We derive generalized bounds on conditional expected excess returns on individual stocks. Bounds are developed in terms of observable risk-neutral quantities, and can be computed anytime using option prices for any available investment horizon. Accounting for all risk-neutral moments of individual stock returns, they outperform runner-up models for in- and out-of-sample return predictions. Calibrated to realized returns, bounds correspond to reasonable preference parameters at various horizons. Average conditional expected returns given by the generalized lower bounds significantly decrease on FOMC days and in the even weeks of the FOMC cycle; stocks with low sensitivity to FOMC news identified using high-order implied moments, beta, idiosyncratic volatility, momentum, among others, experience an increase in expected returns. Further asset pricing tests deliver a reasonable and positive unconditional market risk premium. We also derive bounds on the conditional expected excess log return for individual stocks and perform similar empirical exercises.

  Data for Generalized Lower Bound from 1996 to 2020 [click here]

[13]  The Conditional Expected Market Return, Journal of Financial Economics Volume 137, Issue 3, September 2020, Pages 752-786.  (with Jonnathan Loudis). 

We derive lower and upper bounds on the conditional expected excess market return that are related to risk-neutral volatility, skewness, and kurtosis indexes. The bounds can be calculated in real time using a cross section of option prices. The bounds require a no-arbitrage assumption, but do not depend on distributional assumptions about market returns or past observations. The bounds are highly volatile, positively skewed, and fat tailed. They imply that the term structure of expected excess holding period returns is decreasing during turbulent times and increasing during normal times, and that the expected excess market return is on average 5.2%. 

We also derive closed-form expressions for any physical moment of the excess market return (e.g., mean, variance, skewness, kurtosis, etc.) when the functional form of the utility is specified. We provide closed-form expressions for the SDF obtained when a representative agent has CARA, CRRA, and HARA utilities. In these cases, we also derive closed-form expressions for physical moments of the excess market return. Bounds are not needed. Although we derive these closed-form expressions, our bounds are for the general case when the utility function and SDF are not known. 

[12]  New Entropy Restrictions and the Quest for Better Specified Asset Pricing Models ,  The Journal of Financial and Quantitative Analysis, Volume 54, Issue 6, December 2019, pp 2517-2541. (with Gurdip Bakshi)

[11]  The Term Structure of Co-Entropy in International Financial Markets, Management Science, Volume 65, Issue 8,  August 2019,  3449-3947. (with Riccardo Colacito)

[10]  Crash Sensitivity and the Cross-Section of Expected Stock Returns, with corresponding Online Appendix,  Journal of Financial and Quantitative Analysis, Volume 53, Issue 3, June 2018 , pp. 1059-1100.  (with Stefan Ruenzi and Florian Weigert). 

Data for the LTD 5-1 long-short portfolio return can be found here

[9]  A Recovery that we Can Trust? Deducing and Testing The Restrictions of the Recovery Theorem, The Review of Financial Studies, Volume 31, Issue 2, 1 February 2018, Pages 532–555 . (with Gurdip Bakshi and Xiaohui Gao).

[8]  A New Approach to Measuring Riskiness in the Equity Market: Implications for the Risk Premium, Journal of Banking and Finance, August 2015, 57, 101-117 (with Turan Bali and Nusret Cakici).

[7]  Aggregation of Preferences for Skewed Asset Returns 

Journal of Economic Theory, 154 (2014) pp. 453-489 (with Dietmar Leisen and Eric Renault). 

[6]  Variance bounds on the permanent and transitory components of stochastic discount factors

Journal of Financial Economics, Vol. 105, No 1 July 2012, pp. 191-208. (with Gurdip Bakshi)

[5]  Pricing Kernels with Stochastic Skewness and Volatility Risk

Management Science, Vol. 58, No. 3, March 2012, pp. 624-640.

[4]  A Generalized Measure of Riskiness

Management Science, Vol.57, No. 8, August 2011, pp. 1406-1423.(with Turan Bali and Nusret Cakici)

[3]  Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors 

Journal of Banking and Finance, 2011, vol. 35, 1971-1983.

[2]  Conditioning Information and Variance Bound on Pricing Kernels with Higher-Order Moments: Theory and Evidence

The Review of Financial Studies, 2008, 21 (1): 181-231. 

[1]  State Dependence Can Explain Risk-Aversion Puzzle

The Review of Financial Studies, 2008, 21 (2): 973-1011(with Eric Renault and René Garcia).