A single parameter, termed the mixing fraction, is used to calibrate current localstochastic volatility (LSV) models to traded exotic prices as well as vanilla options. This single parameter has been ...

We consider a p-dimensional time series where the dimension p increases with the sample size n. The resulting data matrix X follows a stochastic volatility model: each entry consists of a positive ...

Affine processes provide a versatile framework for modelling complex financial phenomena, ranging from interest rate dynamics to credit risk and beyond. Their defining characteristic is the affine, or ...

Stochastic volatility is the unpredictable nature of asset price volatility over time. It's a flexible alternative to the Black Scholes' constant volatility assumption.

Stochastic volatility models have revolutionised the field of option pricing by allowing the volatility of an asset to vary randomly over time rather than remain constant. These models have ...

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating ...

This paper introduces stochastic volatility to the Libor market model of interest rate dynamics. As in Andersen and Andreasen (2000a) we allow for nonparametric volatility structures with freely ...

Spot prices in energy markets exhibit special features, such as price spikes, mean reversion, stochastic volatility, inverse leverage effect, and dependencies between the commodities. In this paper a ...

We highlight a state variable misspecification with one accepted method to implement stochastic volatility (SV) in DSGE models when transforming the nonlinear state-innovation dynamics to its linear ...

Section III describes the process of fitting five different Heath, Jarrow, and Morton models to United Kingdom Government Bond yield data: models with 1, 2, 3, 6 and 15 factors. We conclude Section ...