Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
958742 | Journal of Empirical Finance | 2015 | 16 Pages |
•We incorporate high frequency data into copula models of daily asset returns.•High frequency measures significantly improve the fit of dynamic copula models.•High frequency measures significantly improve out-of-sample density forecasts.
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.