Long Memory Dynamics for Multivariate Dependence Under Heavy Tails
Tinbergen Institute Discussion Paper 11-175/5/DSF28
43 Pages Posted: 14 Dec 2011
Date Written: December 9, 2011
Abstract
We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the volatilities of the equity return series is similar, while the degree of memory in correlations between the series varies significantly. The forecasts from our model are compared with high-frequency realized volatility and dependence measures. The forecast accuracy is overall higher compared to those from some well-known competing benchmark models.
Keywords: fractional integration, correlation, student's t copula, time-varying dependence, multivariate volatility
JEL Classification: C10, C22, C32, C51
Suggested Citation: Suggested Citation
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