Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance

Manabu Asai and Michael McAleer

No 14-037/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage Effects; Long Memory; Realized Volatility (search for similar items in EconPapers)
JEL-codes: C32 C53 C58 G17 (search for similar items in EconPapers)
Date: 2014-03-17
New Economics Papers: this item is included in nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance (2015)
Working Paper: Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance (2014)
Working Paper: Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance (2014)
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