A two-step estimation method of stochastic volatility models is proposed: In the first step, we estimate the …, standard estimation methods for fully observed diffusion processes are employed, but with the filtered volatility process … replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility …

We develop a nonparametric estimator of the stochastic volatility density of a discretely-observed Ito semimartingale … underlying volatility Laplace transform. The second step is using a regularized kernel to invert the realized Laplace transform … important cases such as level jumps and possible dependencies between volatility moves and either diffusive or jump moves in the …

-varying volatility of the data generating process can have rather adverse effects when inferring about its persistence; e.g. unit root … conditional heteroskedasticity). The paper explores the influence of time-varying volatility on fractionally integrated processes …. Concretely, we discuss how to model long memory in the presence of time-varying volatility, and analyze the effects of such …

We introduce a statistical test for simultaneous jumps in the price of a financial asset and its volatility process … volatility jumps, we design and analyze a nonparametric spectral estimator of the spot volatility process. A simulation study and … important role played by price volatility co-jumps. …

A two-step estimation method of stochastic volatility models is proposed. In the first step, we nonparametrically … estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed … estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps …