Predicting the “Value at Risk” of a portfolio of stocks is of great significance in quantitative finance. We introduce a new class models, “dynamical products of experts” that treats the latent process over volatilities as an inverse Gamma process. We show that our multivariate volatility models significantly outperform all related Garch and stochastic volatility models which are in popular use...

The paper studies a factor GARCH model and develops test procedures which can be used to test the number of factors needed to model the conditional heteroskedasticity in the considered time series vector. Assuming normally distributed errors the parameters of the model can be straightforwardly estimated by the method of maximum likelihood. Inefficient but computationally simple preliminary esti...

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is ...

Herein, we propose a novel hybrid method for forecasting steel prices by modeling nonlinearity and time variations together to enhance adaptability. The multivariate empirical mode decomposition (MEMD)–ensemble-EMD (EEMD) approach was employed preprocessing separate the nonlinear variation components of hot-rolled coil (HRC) price return series, particle swarm optimization (PSO)-based least squ...

This paper derives an analytical expression for an impulse-response function for a vector autoregression with multivariate GARCH errors, where the vector of conditional means is a function of the conditional variances. We also provide the appropriate interpretation of an impulse-response function for such models and suggest interesting empirical issues that can be addressed within this framework.

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...