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 co...

This study investigates the daily co-movements in commodity prices over period 2006–2020 using a novel approach based on time-varying Gerber correlation. The statistic is computed considering set of probabilities estimated via non-traditional models that give structure to measure. results indicate there are several across commodities, these change time, and they tendentially positive. Condition...

This study considers the effects of the financial institutions’ local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial n...

This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from the NYSE and compare 125 model based one, five and twenty-day ahead conditional variance forecasts over a period of 10 years using the Model Confidence Set (MCS) and the Superior Pr...

This study focuses on the relation between fluctuation of international oil prices and China’s energy stock market during COVID-19 pandemic, using a dynamic conditional correlation generalized autoregressive heteroskedasticity model. We confirm spillover effect volatility price returns determine that leadership has been heavily influenced pandemic.

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...