This paper proposes an improved procedure for stochastic volatility model estimation with an application to Value … components: Fourier transform method for volatility estimation, and importance sampling for extreme event probability estimation …

This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by … the volatility dynamics dVt = κt.(θt − Vt).dt λt.Vt.dBt. This non-affine model is much more realistic than classical … affine models like the Heston stochastic volatility model, even though both are as parsimonious (only four stochastic …

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 …

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 …

implied volatility smile and the risk neutral density function is tested on SPX options …

approximation to provide an improved swaption volatility approximation, and compare this to the approaches of Rebonato, Hull …

inference problem of estimating an option pricing model which displays two stochastic volatility factors, allows for co …-jumps between price and volatility, and stochastic jump intensity. Furthermore, we consider real data and estimate the model on a …

local volatility surface for S&P500 option data …