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...

We estimate the risk spillover among European banks from equity log-return data via Conditional Value at Risk (CoVaR). The joint dynamic of returns is modeled with a spatial DCC-GARCH which allows conditional variance log-returns each bank to depend on past volatility shocks other and their squared in parsimonious way. backtesting resulting measures provides evidence that (i) multivariate GARCH...

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly ...

Economic and financial time series typically exhibit time varying conditional (given the past) standard deviations and correlations. The conditional standard deviation is also called the volatility. Higher volatilities increase the risk of assets, and higher conditional correlations cause an increased risk in portfolios. Therefore, models of time varying volatilities and correlations are essent...

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...

This brief note offers an explicit algorithm for a multivariate GARCH model, called PC-GARCH, that requires only univariate GARCH estimation. It is suitable for problems with hundreds or even thousands of variables. PC-GARCH is compared to two other techniques of getting multivariate GARCH using univariate estimates.