Continuous-Time Fama-MacBeth Regressions
将经典Fama-MacBeth回归扩展到连续时间因子模型，利用高频收益识别和估计连续成分与不同大小跳跃的风险溢价，实证分析美国股票、外汇和商品市场。
Abstract We develop an asymptotic framework for conducting inference on continuous-time asset pricing models using high-frequency returns over an increasing time horizon. Our study focuses on the identification and estimation of risk premiums associated with the continuous component and jumps of various size brackets. We extend the classical Fama-MacBeth regression from the discrete-time setting to a continuous-time factor model, incorporating general dynamics for factors, idiosyncratic components, and factor loadings. Our empirical analysis of U.S. equities, foreign exchange, and commodities underscores the distinct significance of continuous and jump risk premiums for the specific factors constructed within each asset class in determining expected returns.