Inference on Risk Premia in Continuous-Time Asset Pricing Models

Abstract

We develop and implement asymptotic theory to conduct inference on continuous-time asset pricing models using individual equity returns sampled at high frequencies over an increasing time horizon. We study the identification and estimation of risk premia for the continuous and jump components of risks. Our result generalize the Fama-MacBeth two-pass regression approach from the classical discrete-time factor setting to a continuous-time factor model with general dynamics for the factors, idiosyncratic components and factor loadings, while accounting for the fact that the inputs of the second-pass regression are themselves estimated in the first pass.

Keywords: Two-pass regression, cross-section of expected returns, arbitrage pricing theory, high frequency data, long horizon, semimartingales