We develop a new efficient and analytically tractable method for estimation of parametric volatility models that is …-day data into the Realized Laplace Transform of volatility, which is a model-free and jump-robust estimate of daily integrated … empirical Laplace transform of the unobservable volatility. The estimation then is done by matching moments of the integrated …

Brownian motion. -- asymptotic uniformity ; local limit theorem ; volatility …

virtually any stochastic volatility model model can be approximated arbitrarily well by a carefully chosen continuous time … illustrates these contributions of the paper, estimating a stochastic volatility jump diffusion model …

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time … method. The support for a stochastic volatility model including jumps in both prices and volatility is strong and the model …

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

In this paper we propose a stochastic volatility model for crude oil markets that has the particularity to feature a … OVX volatility data. The model characterizes two states: a normal state with low volatility and negative variance premium … and acrisis state with high volatility and positive variance risk premium. The estimated states are consistent with GDP …

conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility. …