Dynamic copula models and high frequency data
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2015-01-01
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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.
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Patton, AJ, and I De Lira Salvatierra (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 https://hdl.handle.net/10161/13127.
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Andrew J. Patton
Patton’s research interests lie in financial econometrics, with an emphasis on forecasting volatility and dependence, forecast evaluation methods, high frequency financial data, and the analysis of hedge funds and mutual funds. His research has appeared in a variety of academic journals, including the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Econometrica, Journal of Econometrics, and the Journal of the American Statistical Association. He has given hundreds of invited seminars around the world, at universities, central banks, and other institutions. A complete list of his current and past research is available at: http://econ.duke.edu/~ap172/research.html
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