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: Suggested Citation