Welcome! I am an economics PhD candidate at the University of Pennsylvania. My interests lie at the intersection of econometrics, finance, and machine learning. Every day the news reminds us that we live in a complex, ever-changing world. Against that background, I study the econometrics of the interaction between time-varying uncertainty and learning. In particular, I develop parsimonious nonparametric methods for estimating risk in real time.
In my job market paper, Jumps, Realized Densities, and News Premia, I build an interpretable nonparametric framework relating high-frequency and daily returns. This framework provides a novel sufficient statistic for time-varying news risk — jump volatility — which I estimate using high-frequency data. I use this to identify how investors' aversion to uncertainty interacts with new information to drive risk premia. In Bypassing the Curse of Dimensionality: Feasible Multivariate Density Estimation, I develop Bayesian estimators for the joint density of several variables and provide new theory showing why this is doable in practice. In Identification Robust Inference for Risk Prices in Structural Stochastic Volatility Models, I provide identification-robust inference for the prices of market and volatility risk in the presence of stochastic volatility and leverage effects.
I currently am on the job market and will be available for interviews at the 2019 ASSA Annual Meeting in Atlanta, Georgia.