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.
New: August, 2022
Conferences: 8th BI-SHoF Conference (2022)
A value-weighted portfolio of US stocks is not a well-diversified portfolio. While a substantial amount of the variation in the index can be explained by a single dominant factor (the first principal component of a large set of characteristic-sorted portfolios), index returns are also driven by nontrivial, time-varying exposures to weaker factors and "granular residuals" - idiosyncratic shocks to large firms that aren't diversified away. We argue, both theoretically and empirically, that these additional components can generate instability in tests of the risk-return tradeoff. Then, we reevaluate the current consensus for a weak market risk-return tradeoff in the US stock market using an alternative index unaffected by them. In the time series, we find stronger evidence of a relation between the risk premium and variance of the market after these corrections. In the cross-section, we find evidence that making these corrections generates larger spreads in market betas, and that this exposure to market risk explains a much larger share of variation in expected returns. Finally, in line with our theory, correcting for these errors eliminates the ability of size factors to improve pricing within a large set of standard factor models.
Updated: May, 2022
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 returns implied by analyst price targets and provide evidence that the market does not efficiently incorporate these components into prices. Prices underreact to the information component and returns drift in the direction of their initial reaction for up to 12 months. Prices overreact to the bias component and reverse their initial reaction within three to six months. Market participants are able to partially debias analyst-expected returns before incorporating them into prices with the initial reaction to bias being much weaker than that to information. These effects are economically significant as evidenced by implementable trading strategies.
Updated: April, 2022
Conferences and Workshops: American Finance Association (AFA) Annual Meeting (2022), Midwest Finance Association (2022), FMA Conference on Derivatives and Volatility (2021), Northern Finance Association (NFA) Annual Meeting (2021), Wabash Conference (2021), Virtual Derivatives Workshop (03-24-2021)
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 the functional form of 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 set of new empirical facts regarding sources of conditional risk premia and identify new challenges for representative agent models.
Updated: April, 2022
Conferences and Workshops: China International Conference in Finance (2022, scheduled), University of Southern California Macro-Finance Workshop (2022), Midwest Finance Association (2021), Luso-Brazilian Finance Meeting (2021)
We develop a factor model that is tightly linked to intertemporal asset pricing theory. Specifically, we show that a long-term Bayesian investor prices shocks to the market dividend yield and realized variance as they reflect news to long-term expected returns and volatility. Accordingly, we construct intertemporal risk factors as long-short portfolios based on stock exposures to dividend yield and realized variance, and estimate their risk prices, which are consistent with the ICAPM under moderate risk aversion. Our intertemporal factor model performs well relative to previous models in terms of its tangency Sharpe ratio and its pricing of key test assets.
Work in Progress
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.