The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the … jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive … definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from …

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 …

standard inference from least-squares estimation of a suitably adjusted predictive regression. We analyze US and international …

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 …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of …