The traditional approach to stochastic volatility (SV) modelling begins with the specification of an SV process, typically on the grounds of its analytical tractability (see, for example, Heston, 1993 ...

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 examines the application of various stochastic volatility models to real data and demonstrates their effectiveness in calibrating a wide range of options, including those with short-term ...

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 ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...

This article empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the ...

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those ...

Options are a financial instrument that give the holder the right to buy and sell an underlying asset, at a predetermined price, on or before a specified date. For example, European-style options ...