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 ...
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 ...
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 ...
Andersen, T., Chung, H., and Sorensen, B. (1999). Efficient method of moments estimation ofa stochastic volatility model: a Monte Carlo study. Journal of Econometrics ...
We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more general non-Ornstein-Uhlenbeck models. In particular, we investigate the means of making the ...
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 ...
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 ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results