We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH...

We apply univariate GARCH models to construct a computationally simple filter for estimating the conditional correlation matrix of asset returns. The proposed Variance Implied Conditional Correlation (VICC) exploits the polarization result that links the correlation between two standardized...

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...

Large once-off events cause large changes in prices but may not affect volatility and correlation dynamics as much as smaller events. Standard volatility models may deliver biased covariance forecasts in this case. We propose a multivariate volatility forecasting model that is accurate in the...