data from the Heston (1993) stochastic volatility model. From these simulated data we create daily returns, which are in …

Techniques for simulated maximum likelihood (SML) estimation, filtering, and assessing the fit of stochastic volatility … single-factor models adequately capture the dynamics of volatility; the problem is to get the shape of the returns … distribution right. Although including a second volatility factor improves the fit over the basic single-factor models, a new …

volatility, high persistence and smoothness. With the quasi-ML approach proposed in our study, we showed that volatility is far …

stochastic volatility, and (iii) the specification of the volatility process itself. We then consider a variety of model … movement and whether stochastic volatility comes from jump or diffusion. We find that, to capture the behavior of the S&P 500 …

empirical performance of this algorithm is considered within the context of the stochastic volatility model. It is found that … the proposed algorithm outperforms a number of accepted procedures in terms of volatility forecasti …

significant in volatility as opposed to expected returns. This paper seeks an explanation for this empirical finding by … volatility model, in which the conditional daily volatility is measured in calendar time from open-to-close of the market, and … over weekends and especially holidays is a predictor of subsequent daily volatility. The SV parameters are estimated by …

The stochastic volatility (SV) models had not been popular as the ARCH (autoregressive conditional heteroskedasticity …) approximates the marginal likelihood of the observable process by simulating the latent volatility conditional on the available … information. Shephard and Pitt (1997) gave an idea of evaluating likelihood by exploiting sampled volatility. Durbin and Koopman …