volatility (SV) models. Whenever structural changes in some model parameters increase the empirical autocorrelations of the … squares of the underlying time series, the persistence in volatility implied by the estimated model parameters follows suit …. This explains why stochastic volatility often appears to be more persistent when estimated from a larger sample as then the …

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

The stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between … the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model based on … returns and volatility. The new model is estimated by the efficient importance sampling method of Liesenfeld and Richard (2003 …

tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization …

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility … clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time … superior next trading day's realized volatility forecasts …

In this paper, I study the drop of real GDP volatility which has been observed in the United States during the postwar … moderation ; Sectoral Shifts ; Stochastic Volatility ; Wishart Autoregressive Process ; Particle Filter ; ARCH-GARCH ; Bayesian …

most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather … good on the present set of measures as the stochastic volatility models, with or without dynamic correlation. …