Dynamic copula models and high frequency data
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© 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.
Published Version (Please cite this version)10.1016/j.jempfin.2014.11.008
Publication InfoDe Lira Salvatierra, I; & Patton, Andrew J (2015). Dynamic copula models and high frequency data. Journal of Empirical Finance, 30. pp. 120-135. 10.1016/j.jempfin.2014.11.008. Retrieved from http://hdl.handle.net/10161/13127.
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Zelter Family Professor
Professor Patton’s research areas include econometrics, financial economics and forecasting. His work focuses on improved models for risk and dependence between financial assets, methods for forecast evaluation and comparison, and empirical asset pricing. Patton's recent publications include "Simulated Method of Moments Estimation for Copula-Based Multivariate Models" (2013, Journal of the American Statistical Association, joint with Dong Hwan Oh), "On the High Frequency Dynamics of Hedge Fund R