We propose a simple but rigorous stochastic volatility – stochastic correlation model, formulated as a pair of … correlated CIRCEV and Jacobi processes. Our model proves to fit both marginal and joint distributions of implied volatility and …

Affine jump diffusion models in general and affine stochastic volatility models in particular are important modeling … stochastic volatility model, nested in the former as a special case, this paper collects explicit expressions for various …

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 …

This article proposes a simple and intuitive framework to combine a discrete volatility forecast series produced by a …-combining binomial trees that capture the distributional properties of the volatility forecasts. Finally, the framework is employed to …

We propose a class of discrete-time stochastic volatility models that, in a parsimonious way, captures the time …

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of … discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized … realized measures in inflating the latent volatility persistence - the crucial parameter in pricing Standard and Poor's 500 …

volatility to capture the dynamics of the S&P 500 and three European equity indices. The stochastic volatility models are the … square root variance, GARCH, and log volatility diffusions, and each is augmented with price and volatility jump extensions … that GARCH diffusions augmented with correlated price and volatility jumps outperform other specifications with respect to …

Derivatives, especially equity and volatility options, contain valuable and oftentimes essential information for … estimating stochastic volatility models. Absent strong assumptions, their typically highly nonlinear pricing dependence on the … jointly accounts for stock returns as well as prices of equity and volatility options. Finally, we provide numerical results …

We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of … proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network …

Numerous empirical proofs indicate the adequacy of the time discrete auto-regressive stochastic volatility models … corresponding market and the non-observability of the associated volatility process. In this paper we introduce new pricing kernels … for this setup and apply two existing volatility filtering techniques available in the literature for these models, namely …