## Paul Sangrey

Welcome! I am an economist at the University of Pennsylvania. I will be taking a position at Amazon in July 2019. My interests lie at the intersection of econometrics, finance, and machine learning. We live in a world where vast new datasets are becoming available every day. This lets us rigorously identify many objects of interest for the first time. Against that background, I develop parsimonious nonparametric methods for estimating densities and other unobserved complex objects.

In *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. In *Bypassing the Curse of Dimensionality: Feasible Multivariate Density Estimation*, I develop Bayesian methods to feasibly estimate the joint density of 5 to 10 variables and provide new theory to explain why this is actually doable. 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.