estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part …

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 …

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 …

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

estimation of the state vector and of the time-varying parameters. We use this method to study the time-varying relationship …

estimation of the state vector and of the time-varying parameters. We use this method to study the timevarying relationship …