Dynamic copula models and high frequency data

Irving De Lira Salvatierra and Andrew Patton

Journal of Empirical Finance, 2015, vol. 30, issue C, 120-135

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. (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.

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)
Date: 2015
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Citations: View citations in EconPapers (51)

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Working Paper: Dynamic Copula Models and High Frequency Data (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:30:y:2015:i:c:p:120-135

DOI: 10.1016/j.jempfin.2014.11.008

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