**Published Papers**

**Published Papers**

**Journal of Financial Economics**137 (3), September 2020, Pages 752-786.Bounds data from the paper

Internet Appendix to accompany the paper

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.

**Working Papers**

**Working Papers**

**Updated:***March 2021**Best Paper in Asset Pricing: 2019 SFS Cavalcade Asia-Pacific**Winner: 2019 Chicago Quantitative Alliance Academic Paper Competition**Conferences: SFS Cavalcade Asia-Pacific (2019), Midwest Finance Association (2019), Chicago Quantitative Alliance (2019), Miami Behavioral Finance Conference (2018, PhD poster session), Illinois Economic Association (2018)*

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. Using a Bayesian updating paradigm, I find evidence that market participants are able to partially (but not fully) debias analyst-expected returns before updating their own expectations about future returns.

**New:***December 2020**Conferences:**Virtual Derivatives Workshop (Scheduled)*

We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into components related to contingent claims on down, up, and moderate market returns. The decompositions do not depend on assumptions about investor preferences, nor do they depend on assumptions about the market return distribution. Analogous decompositions implied by prominent representative agent models fail to match those implied by the data. Our results provide a host of new empirical facts regarding sources of conditional risk premia and identify a set of new challenges for representative agent models.

**New:***September 2020**Conferences: Midwest Finance Association (2021, Scheduled),**Luso-Brazilian Finance Meeting (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**

**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.