Journal of Computational Finance

Christoph Reisinger
University of Oxford

It is a pleasure to introduce the latest issue of The Journal of Computational Finance. The contributions herein focus on different product classes and extend the state-of-the- art in modeling and computation.

In our first paper, “An explicit scheme for pathwise cross valuation adjustment computations”, Lokman Abbas-Turki, Stéphane Crépey, Botao Li and Bouazza Saadeddine model cross valuation adjustments in the presence of variation margins using anticipated backward stochastic differential equations, and they present a numerical scheme based on neural network least squares and quantile regressions. The authors demonstrate favourable computational performance compared with benchmarks in an application with a large number of risk factors.

Next, in their paper “A flexible commodity skew model with maturity effects”, Orcan Ögetbil and Bernhard Hientzsch introduce an extension of Andersen’s commodity skew model with additional control of the volatility smile and accumulation of total variance. They demonstrate efficient and accurate calibration for crude oil and natural gas using the pricing of Asian options and the computation of expected exposures as use cases.

Last but not least, in “Convexity adjustments à la Malliavin”, the issue’s third paper, David Garcia-Lorite and Raúl Merino present a generic methodology for the approximation of convexity adjustments based on Malliavin calculus. They carry out explicit computations for various common interest rate products in a class of one-factor Cheyenne models, which can serve as a template for other products and models. The paper’s numerical results demonstrate high accuracy.

I wish you an enjoyable read.