Measuring and modeling systematic risk in factor pricing models using high-frequency data

Published

Journal Article

This paper demonstrates how high-frequency data may be used in more effectively measuring and modeling the systematic risk(s) in factor pricing models. Based on a 7-year sample of continuously recorded US equity transactions, we find that simple and easy-to-implement time series forecast for the high-frequency-based factor loadings in the three-factor Fama-French model gives rise to more accurate factor representations and improved asset pricing predictions when compared to the conventional monthly rolling regression-based estimates traditionally employed in the literature, in turn resulting in more efficient ex post mean-variance portfolios. As such, the methodology proposed in the paper holds the promise for important new insights concerning actual real-world investment decisions and practical situations involving risk management. © 2003 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Bollerslev, T; Zhang, BYB

Published Date

  • January 1, 2003

Published In

Volume / Issue

  • 10 / 5

Start / End Page

  • 533 - 558

International Standard Serial Number (ISSN)

  • 0927-5398

Digital Object Identifier (DOI)

  • 10.1016/S0927-5398(03)00004-5

Citation Source

  • Scopus