memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the …

vector autoregressive (GVAR) specification with drifting coefficients and factor stochastic volatility in the errors to model …

vector autoregressive (GVAR) specification with drifting coefficients and factor stochastic volatility in the errors to model …

forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches …, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts …

apply autoregressive moving average models for the conditional means and GARCH and stochastic volatility models for the …

Markov-switching Heston's stochastic volatility model. Under this framework, the expected return and the long-term mean of … evidence that the Markov-switching Heston's stochastic volatility model performs well in capturing major events affecting price …. The saddlepoint method gives an effective approximation to option prices under the Markov-switching Heston's stochastic …

VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine …