RT Report
T1 Forecasting Co-Volatilities via Factor Models withAsymmetry and Long Memory in Realized Covariance
A1 Asai, Manabu
A1 McAleer, Michael
AB 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 onditional/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.
YR 2014
FD 2014-03
LK https://hdl.handle.net/20.500.14352/41555
UL https://hdl.handle.net/20.500.14352/41555
LA eng
NO JEL classifications: C32, C53, C58, G17The authors are most grateful to Yoshi Baba for very helpful comments and suggestions. The first authoracknowledges the financial support of the Japan Ministry of Education, Culture, Sports, Science and Technology,Japan Society for the Promotion of Science, and Australian Academy of Science. The second author is mostgrateful for the financial support of the Australian Research Council, National Science Council, Taiwan, and theJapan Society for the Promotion of Science. Address for correspondence: Faculty of Economics, Soka University,1-236 Tangi-cho, Hachioji, Tokyo 192-8577, Japan.
DS Docta Complutense
RD 7 abr 2025