Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87332
Authors: 
Sokolinskiy, Oleg
van Dijk, Dick
Year of Publication: 
2011
Series/Report no.: 
Tinbergen Institute Discussion Paper 11-125/4
Abstract: 
This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious way. In an empirical application to daily volatility for S&P500 index futures, we find that the copula-based RV (C-RV) model outperforms conventional forecasting approaches for one-day ahead volatility forecasts in terms of accuracy and efficiency. Among the copula specifications considered, the Gumbel C-RV model achieves the best forecast performance, which highlights the importance of asymmetry and upper tail dependence for modeling volatility dynamics. Although we find substantial variation in the copula parameter estimates over time, conditional copulas do not improve the accuracy of volatility forecasts.
Subjects: 
Nonlinear dependence
long memory
copulas
volatility forecasting
JEL: 
C22
C53
C58
G17
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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