Journal of Computational Finance

Christoph Reisinger
University of Oxford

I am pleased to introduce the September 2021 issue of The Journal of Computational Finance.

This issue has two themes running through it: machine learning and fixed income. This is exemplified by our first paper, “An artificial neural network representation of the SABR stochastic volatility model”, in which William A. McGhee demonstrates how artificial neural networks can be trained to accurately reproduce option prices under the SABR stochastic volatility model. The online evaluation of the trained surrogate model takes a small fraction of the time of a state-of-the-art partial differential equation solver and has negligible bias compared with the standard SABR expansion formula.

An accurate approximation formula for the Heston–Hull–White foreign exchange model is then proposed by Kenji Nagami in “Expansion method for pricing foreign exchange options under stochastic volatility and interest rates”, the issue’s second paper. Exploiting small volatility-of-volatility, a second-order analytical expansion is shown to match numerical benchmark solutions well even in regimes where other methods fail.

Simon Fecamp, Joseph Mikael and Xavier Warin propose machine learning approaches to optimal hedging under market frictions in the third paper in this issue: “Deep learning for discrete-time hedging in incomplete markets”. The authors compare a global optimization technique with a local dynamic programming technique, and their numerical studies find that the former approach gives a superior performance, which ultimately enables the authors to construct optimal frontiers.

In our final paper, “Quantization-based Bermudan option pricing in the foreign exchange world”, Jean-Michel Fayolle, Vincent Lemaire, Thibaut Montes and Gilles Pagès construct optimal quantizations of exchange rate processes with stochastic rates, which facilitate a dynamic programming argument for Bermudan-style contracts. Numerical tests validate the method and show its efficiency.

I hope you will enjoy learning about these exciting new research directions as much as I have

 

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: