Yicheng Zhu is a fifth-year Ph.D. student in Finance at the Wharton School, University of Pennsylvania. His research interests include asset pricing, macro-finance, and econometrics.
His job market paper focuses on the disconnection between the riskiness and risk premium of assets, especially in cross-section. For example, long-maturity riskfree real zero-coupon bonds provide hedges against long-run shocks in the economy but feature a higher premium in the data. Yicheng shows that by allowing the agent to show distinct levels of risk aversion toward risks of different types, the agent has different preference to the timing of uncertainty resolution to different shocks, and this mechanism can address a very large body of puzzles in the term structure, and the beta anomaly.
His other work investigates the implication of imperfect information on asset prices. One of his recent working papers builds a quantitative model to explain the announcement premium. On days when the U.S. government or Fed makes the announcement of the macro-economy, a large proportion of equity premium is realized. In addition, there is a strong CAPM on those days, while aggregate risk, measured by the volatility, does not increase. The paper models that the US government provides critical information about rare disasters on those pre-scheduled days, and can successfully quantitatively explain the announcement premium.
He joined the Wharton Finance Ph.D. program in 2015. Before Wharton, he obtained an A.M. in Statistics from Harvard and a B.Sc. in Mathematics and Applied Mathematics from Peking University.
Abstract: Financial crises appear to have long-lasting effects, even after the
crisis itself has past. This paper offers a simple explanation
through Bayesian learning from rare events. Agents face a latent and time-varying
probability of economic disaster. When a disaster occurs, learning
results in greater effects on asset prices because agents update their
probability of future disasters. Moreover, agents' belief that the
disaster risk is high can rationally persist for years, even when it
is in fact low. We generalize the model to allow for a noisy signal of
the disaster probability. This generalized model explains excess
stock market volatility together with negative skewness, effects that
previous models in the literature struggle to explain.