Dynamic Copula Models and High Frequency Data

Published

Journal Article (Working Paper)

© 2014 Elsevier B.V.This paper proposes a new class of dynamic copula models for daily asset returns that exploits information from high frequency (intra-daily) data. We augment the generalized autoregressive score (GAS) model of Creal et al. (2013) with high frequency measures such as realized correlation to obtain a "GRAS" model. We find that the inclusion of realized measures significantly improves the in-sample fit of dynamic copula models across a range of U.S. equity returns. Moreover, we find that out-of-sample density forecasts from our GRAS models are superior to those from simpler models. Finally, we consider a simple portfolio choice problem to illustrate the economic gains from exploiting high frequency data for modeling dynamic dependence.

Full Text

Duke Authors

Cited Authors

  • Patton, AJ; De Lira Salvatierra, I

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 30 /

Start / End Page

  • 120 - 135

International Standard Serial Number (ISSN)

  • 0927-5398

Digital Object Identifier (DOI)

  • 10.1016/j.jempfin.2014.11.008