volatility. The study investigates whether the asymmetric effects of good and bad news on volatility is present and how … distributional assumptions affect the selection of GARCH models. Compared to two widely used historical volatility models, the simple … the forecasts of loan market volatility. The model comparison involves a regression-based approach, loss functions and …

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 …

simultaneously estimated. Results with realized volatility, volumes and number of trades of the JNJ stock show that significantly … superior realized volatility forecasts are delivered with a fully interdependent vMEM relative to a single equation …

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

This paper studies predictability of realized volatility of U.S. Treasury futures using high-frequency data for 2-year … to generate systemic under-predictions of future realized volatility. …

formulate a volatility forecast of returns used as an input for determining some subjective views to be included in the Black …

section compares stochastic volatility models with GARCH. …

the benchmark (MSCI EFM (World) index) weights. MSCI previously stood for Morgan Stanley Capital International. The … best. Also, the volatility forecasts generated from multivariate time series models can be successfully converted into … higher portfolio returns using quantitative investment approaches if the right balance of volatility modelling and portfolio …