Continuous-Time Fama-MacBeth Regressions

77 Pages Posted: 17 Sep 2020 Last revised: 14 Oct 2024

See all articles by Yacine Ait-Sahalia

Yacine Ait-Sahalia

National Bureau of Economic Research (NBER); Princeton University - Department of Economics

Jean Jacod

Université Paris VI Pierre et Marie Curie

Dacheng Xiu

University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: October 09, 2024

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 premia associated with 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 premia for the specific factors constructed within each asset class in determining expected returns.

Keywords: Fama-MacBeth, two-pass regression, cross-section of expected returns, arbitrage pricing theory, high frequency data, semimartingales

Suggested Citation

Ait-Sahalia, Yacine and Jacod, Jean and Xiu, Dacheng, Continuous-Time Fama-MacBeth Regressions (October 09, 2024). Chicago Booth Research Paper No. 20-30, Available at SSRN: https://ssrn.com/abstract=3692604 or http://dx.doi.org/10.2139/ssrn.3692604

Yacine Ait-Sahalia

National Bureau of Economic Research (NBER)

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HOME PAGE: http://www.princeton.edu/~yacine

Jean Jacod

Université Paris VI Pierre et Marie Curie ( email )

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Dacheng Xiu (Contact Author)

University of Chicago - Booth School of Business ( email )

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Chicago, IL 60637
United States

National Bureau of Economic Research (NBER) ( email )

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United States

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