High-dimensional copula-based distributions with mixed frequency data

Published

Journal Article

© 2016 Elsevier B.V. This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In- and out-of-sample tests confirm the superiority of the proposed models applied to daily returns on constituents of the S&P 100 index.

Full Text

Duke Authors

Cited Authors

  • Oh, DH; Patton, AJ

Published Date

  • August 1, 2016

Published In

Volume / Issue

  • 193 / 2

Start / End Page

  • 349 - 366

Electronic International Standard Serial Number (EISSN)

  • 1872-6895

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/j.jeconom.2016.04.011

Citation Source

  • Scopus