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 …

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 …

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 …