forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures … outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the … Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten …

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with …, both in simulation and when forecasting a large cross section of industry portfolios spanning almost a hundred years of …

realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in …

volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate … leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that …

. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were …

realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation … and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed …

augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi …

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities …