volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …

can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation.In order to simplify the …

In this article we introduce a linear quadratic volatility model with co-jumps and show how to cal- ibrate this model … simultaneous jumps in both re- turn process and volatility process and the superposition structure of a continuous linear quadratic … volatility process and a Lévy-driven Ornstein-Uhlenbeck process. We compare the quality of fit for several models, and show that …

We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility …. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long …-run component that changes at daily frequency and a short-run component that captures the remaining intraday volatility dynamics. An …

-portfolio specific volatility indices called portfolio risk drivers. The dynamics of the risk drivers are modelled by multiplicative …. The proposed risk drivers capture the volatility structure of asset returns in different industry sectors. A …