The existing parametric multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model could hardly capture the nonlinearity and the non-normality, which are widely observed in …nancial data. We propose semiparametric conditional covariance (SCC) model to capture the information hidden in the standardized residuals and missed by the parametric MGARCH models. Our two-stage...

Abstract Estimating time-varying conditional covariance matrices of financial returns play important role in portfolio analysis, risk management, and econometrics research. The availability high-frequency data can provide an additional source for dynamic modeling. In this paper, we propose to use the information asset return vector realized measures simultaneously develop a new matrix model. We...

A joint fractionally integrated, error-correction andmultivariateGARCH (FIEC-BEKK) approach is applied to investigate hedging effectiveness using daily data 1995–2005. The findings reveal the proxied error-correction term has a long memory component that theoretically should affect hedging effectiveness.When the FIECmodel empirical conditions are satisfied, the FIEC-BEKK hedging strategy outper...

The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents a...

Abstract For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number parameters cope with ‘curse dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal Econometrics 179 : 16–30) developed rotated GARCH model, which focuses on for standardized variables. This paper ext...

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances correlations of daily returns based on measures realized built from intraday data. Formulas multi-step forecasts are provided. Asymmetric versions developed. An empirical study shows that in terms HEAVY outperform BEKK-HEAVY model covariances BEKK, DCC, DECO multivariate GARCH exclusively

The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents a...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

This paper investigates the hedging effectiveness of time-varying hedge ratios in the agricultural commodities futures markets based on four different versions of the GARCH models. The GARCH models applied are the standard bivariate GARCH, the bivariate BEKK GARCH, the bivariate GARCH-X and the bivariate BEKK GARCH-X. The GARCH-X and the BEKK GARCH-X models are uniquely different from the other...