dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

average daily returns, even though the volatility is virtually unchanged when the frequency is lower. The volatility from the … highest to the lowest frequency is about 30% lower as compared with the buy-and-hold strategy volatility, but the average … returns approach the buy-and-hold returns when frequency is lower. The 30% reduction in volatility appears if we invest …

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 …