The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized … covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions …

This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model …

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process …. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple … setting. The volatility forecast function follows a second-order difference equation as opposed to first-order under GARCH(1 …

the squared return in the volatility process. The volatility of the diffusion follows an Ornstein-Uhlenbeck-type process …

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an … extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models … Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

empirical application aiming at comparing estimates and predictions of the volatility of financial asset returns. The Dynamic …