In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data …

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 three most popular univariate conditional volatility models are the generalized autoregressive conditional … models are important in estimating and forecasting volatility, as well as capturing asymmetry, which is the different effects … on conditional volatility of positive and negative effects of equal magnitude, and leverage, which is the negative …

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions … from returns to volatility and co-volatility. …

subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of … increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …