Dynamic Copula Models and High Frequency Data
AbstractThis 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.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 13-28.
Date of creation: 2013
Date of revision:
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Realized correlation; realized volatility; dependence; forecasting; tail risk;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-12-29 (All new papers)
- NEP-ECM-2013-12-29 (Econometrics)
- NEP-ETS-2013-12-29 (Econometric Time Series)
- NEP-FOR-2013-12-29 (Forecasting)
- NEP-MST-2013-12-29 (Market Microstructure)
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- Francesco Calvori & Drew Creal & Siem Jan Koopman & Andre Lucas, 2014. "Testing for Parameter Instability in Competing Modeling Frameworks," Tinbergen Institute Discussion Papers 14-010/IV/DSF71, Tinbergen Institute.
- Francisco Blasques & Siem Jan Koopman & Andr� Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
- Marco Bazzi & Francisco Blasques & Siem Jan Koopman & and Andre Lucas, 2014. "Time Varying Transition Probabilities for Markov Regime Switching Models," Tinbergen Institute Discussion Papers 14-072/III, Tinbergen Institute.
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