The Journal of Financial Economics, 2020
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.
Updated: January 2021
Best Paper in Asset Pricing: 2019 SFS Cavalcade Asia-Pacific
Winner: 2019 Chicago Quantitative Alliance Academic Paper Competition
I use a novel decomposition to extract information and bias components from the expected returns implied by analyst price targets and provide evidence that the market does not efficiently incorporate these components into prices. Prices overreact to the bias component and reverse their initial reaction within three to six months. Prices underreact to the information component and returns drift in the direction of their initial reaction for up to 12 months. Trading against underreaction generates average monthly returns of 1.12% with a Sharpe ratio of 1.08. These average returns survive controlling for exposure to many standard factors.
New: December 2020
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 the 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.
New: September 2020
Midwest Finance Association 2021 (Scheduled)
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.
Work in Progress
"Idiosyncratic Labor Income in a Production General Equilibrium Model" (with Miguel Palacios and Lawrence Schmidt)
We develop a highly tractable, general equilibrium model with production and incomplete markets. In the model, agents can invest in physical capital and human capital, where the latter investment technology is subject to uninsurable, idiosyncratic disaster risk. The quantity of both inputs is time-varying and endogenously determined in equilibrium, subject to aggregate adjustment costs. We demonstrate that the presence of uninsurable risk has first-order implications for the riskiness of human capital; in particular, the risk premium on human capital and the share of total wealth in human capital are considerably larger and smaller, respectively, relative to the complete markets benchmark. Moreover, the presence of state-dependent, idiosyncratic risk increases the equity risk premium and has important implications for agent's optimal investment behavior.