Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/98872
Authors: 
Asai, Manabu
McAleer, Michael
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-037/III
Abstract: 
Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.
Subjects: 
Dimension reduction
Factor Model
Multivariate Stochastic Volatility
Leverage Effects
Long Memory
Realized Volatility.
JEL: 
C32
C53
C58
G17
Document Type: 
Working Paper

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