t he relationship between the price of oil and the level of economic activity is a fundamental empirical issue in macroeconomics. in this research, by using a multivariate garch-in-mean var, we try to investigate direct effects of uncertainty of oil price on macroeconomics of iran by using annually data from 1965 to 2013.results show that uncertainty about oil prices had a negative and signific...

Value at Risk (VaR) has become the industry standard to measure the market risk. However, the selection of the VaR models is controversial. Simulation Results indicate Historical Simulation has significant positive bias, while GARCH (1,1) has has significant negative bias. Also HS adapts structural change slowly but stable, while GARCH adapts structural break rapidly but less stable. Thus the m...

We develop a multivariate generalization of the Markov–switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth– moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including out–of–sample portfolio selection and computation of Value– ...

We establish the validity of subsampling confidence intervals for the mean of a dependent series with heavy-tailed marginal distributions. Using point process theory, we study both linear and nonlinear GARCH-like time series models. We propose a data-dependent method for the optimal block size selection and investigate its performance by means of a simulation study. JEL CLASSIFICATION NOS: C10,...

a r t i c l e i n f o JEL classification: C32 C51 L94 Q40 Keywords: Wholesale spot electricity price markets Constant and dynamic conditional correlation Multivariate GARCH This paper examines the interrelationships of wholesale spot electricity prices among the four regional A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Dynamic c...

This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discrete observations. A numerical solution to the stochastic differential equation is obtained by higher order Wagner-Platen approximation and it is used to derive the first two conditional moments. Monte Carlo simulation shows that the proposed method has good finite sample property for both normal 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...

Article history: Received 20 June 2014 Received in revised form 25 September 2014 Accepted 26 September 2014 Available online 5 October 2014 This paper investigates the time-varying impact of oil price uncertainty on stock prices in China using weekly data on ten sectoral indices over the period January 1997–February 2014. The estimation of a bivariate VAR-GARCH-in-mean model suggests that oil ...

We estimate and test the conditional version of an International Capital Asset Pricing Model using a parsimonious multivariate GARCH process. Since our approach is fully parametric, we can recover any quantity that is a function of the first two conditional moments. Our findings strongly support a model which includes both market and foreign exchange risk. However, both sources of risk are only...

This note investigates impacts of multivariate generalised autoregressive conditional heteroskedasticity (GARCH) errors on hypothesis testing for cointegrating vectors. The study reviews a cointegrated vector autoregressive model incorporating multivariate GARCH innovations and a regularity condition required for valid asymptotic inferences. Monte Carlo experiments are then conducted on a test ...