In this paper we propose a new multivariate GARCH model with timevarying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By impos...

Purpose The purpose of the study is to measure cross-country stock market correlation and volatility transmission during global coronavirus disease 2019 (COVID-19) pandemic. paper traces trajectory Islamic equity investments in order get insights on behavior markets crisis. Design/methodology/approach uses generalized method moments (GMM), autoregressive distributed lag (ARDL) multivariate GARC...

This paper investigates the issue of co-movement and interaction among the monetary, foreign exchange and stock markets by employing the data from China’s financial markets. Based on the ICA-EGARCH-M model, we explore the volatility spillover effects so as to illustrate the overall co-movements across financial markets. Furthermore, in order to observe the multi-market dynamic relationship vari...

We develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset retu...

A Bayesian non-parametric approach for efficient risk management is proposed. A dynamic model is considered where optimal portfolio weights and hedging ratios are adjusted at each period. The covariance matrix of the returns is described using an asymmetric MGARCH model. Restrictive parametric assumptions for the errors are avoided by relying on Bayesian nonparametric methods, which allow for a...

This paper proposes a threshold multivariate GARCH model (Threshold MGARCH) which integrates threshold nonlinearity, mean and volatility asymmetries and time-varying correlation in financial markets. The main feature of this model is that the mean, volatility and time-varying correlation can be governed by different threshold variables with different number of regimes. Estimation is performed u...