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 …

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the … definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from … the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for …

for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out … improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source …

variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk … forecasting. …

volatility forecasting of stock returns and exchange rates. … dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … dynamics of higher-order moments, and to the other preferred choice of forecasting distribution. We apply our method to Value …

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized … forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions …