Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our ne...

In this paper we present a new type of multivariate GARCH model which we call the composed MGARCH and factor composed MGARCH models. We show sufficient conditions for the covariance stationarity of these processes and proof of the invariance of the models under linear combinations, an important property for factor modeling. Furthermore, we introduce an α-stable version of these models and fit a...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinkin...

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...

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncerta...

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...

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 examines hedging effectiveness in Greek stock index futures market. We focus on various techniques to estimate variance reduction from constant and time-varying hedge ratios. For both available stock index futures contracts of the Athens Derivatives Exchange (ADEX), we employ a variety of models to derive and estimate the effectiveness of hedging. We measure hedging effectiveness usi...

â â â  â â â â â  the main purpose of present study is to analyze the relationship between stock and exchange markets in two asian countries, iran and south korea. a monthly time series of stock price and exchange rate are used over the period 2002: 05 - 2012: 03. the data is collected from the central bank of each country and wdi. the calculated stock return and real exchange rate change are u...