Dynamic Copula Models and High Frequency Data

37 Pages Posted: 26 Jun 2013 Last revised: 16 Nov 2013

Date Written: June 24, 2013


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. (2012) 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.

Keywords: Realized correlation, realized volatility, dependence, forecasting, tail risk

JEL Classification: C32, C51, C58

Suggested Citation

De Lira Salvatierra, Irving and Patton, Andrew J., Dynamic Copula Models and High Frequency Data (June 24, 2013). Economic Research Initiatives at Duke (ERID) Working Paper No. 165, Available at SSRN: https://ssrn.com/abstract=2284235 or http://dx.doi.org/10.2139/ssrn.2284235

Irving De Lira Salvatierra

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Andrew J. Patton (Contact Author)

Duke University - Department of Economics ( email )

213 Social Sciences Building
Box 90097
Durham, NC 27708-0204
United States

HOME PAGE: http://econ.duke.edu/~ap172/

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