Dynamic Copula Models and High Frequency Data

Irving Arturo De Lira Salvatierra and Andrew Patton

No 13-28, Working Papers from Duke University, Department of Economics

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C32 C51 C58 (search for similar items in EconPapers)
Pages: 37
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mst
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Citations: View citations in EconPapers (5)

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Journal Article: Dynamic copula models and high frequency data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:13-28

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