Published and Accepted Papers
Journal of Financial Economics 137 (3), September 2020, Pages 752-786.
SSRN link here (includes Internet Appendix).
Bounds data from the paper
NEW (2023): Updated bounds data through Feb/2023. We use updated data to reestimate preference parameters according to the methodology in the paper, and provide the corresponding updated unrestricted and restricted bounds through February, 2023.
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.
Management Science (Published online on November 22, 2023)
SSRN link here (includes Internet Appendix).
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 risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models.
R&R (2nd round): The Accounting Review
Updated: November, 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)
Returns implied by analyst price targets are biased but informative. I use a novel decomposition to extract information and bias components from these analyst-expected returns and provide evidence that prices simultaneously underreact to the information component and overreact to the bias component. Price reactions to information are permanent, and drift in the direction of their initial reaction for up to 12 months. Price reactions to bias are transitory, 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: January, 2023
Conferences and Workshops: China International Conference in Finance (2022), 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.
Updated: January, 2023
Conferences/Presentations: AFA (2024, scheduled), Northern Finance Association (2023), FIRS (2023), Eastern Finance Association (2023), City University of Hong Kong (2022), 8th BI-SHoF Conference (2022), NBER Asset Pricing meeting
In addition to a dominant level factor, stock market index returns contain an “idiosyncratic financial factor” (IFF) unrelated to macroeconomic aggregates. We argue the IFF contaminates tests of the risk-return tradeoff in the time series and cross section, then we reevaluate these tests using an alternative index unaffected by the IFF. Our index generates a stronger relation between its risk premium and conditional variance. It also generates larger cross-sectional variation in market betas, and these exposures explain more variation in expected returns. Our index prices size portfolios and eliminates the pricing power of size factors across many standard factor models.
New: May, 2023
Conferences: American Finance Association (2024, scheduled), Paris December Finance Meeting (2023, scheduled), TAU Finance Conference (2023, accepted but conference canceled)
We use the long-term Capital Market Assumptions of major asset managers and institutional investor consultants from 1987 to 2022 to provide three stylized facts about their subjective risk and return expectations on 19 asset classes. First, the subjective distribution of asset class returns is well described by a 1-factor structure, with this single risk factor typically explaining more than 65% of the subjective variability in asset class returns. Second, at least 80% of the variability in subjective expected returns is due to variability in subjective risk premia (compensation for beta) as opposed to subjective mispricing (alpha). And third, subjective risk and return expectations vary much more across asset classes than across institutions. Our findings imply that models with subjective beliefs should reflect a risk-return tradeoff. Additionally, accounting for this risk-return trade-off is even more important than incorporating belief heterogeneity across institutional investors when modeling multiple asset classes.
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.