volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding …% and at least 78%). The results on the GMV portfolios show that realized covariance models exhibit lower ex-post volatility …

forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures … correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the … outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the …

We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental … about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help … to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are …

the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

conditions have the nice interpretation of restricting the level, slope and curvature of the correlation surface. It is proven … that the Schoenmakers-Coffey correlation matrix also brings along such factors. Finally, we formulate and corroborate our … conjecture that the order present in correlation matrices causes slope. …