Journal of Computational Finance

In this, my first editorial in my new duty as your editor-in-chief for The Journal of Computational Finance, I would like to acknowledge Peter Forsyth's excellent work as the journal's editor-in-chief over the last five years. Peter has left the journal in excellent shape: it is a leading journal (with an official impact factor) on computational methods for advanced models in finance. Thanks a lot, Peter!

We have a diverse range of papers in the present issue, all containing interesting numerical techniques and useful approximations when dealing with multidimensionality in models.

The first paper in this issue is "An n-dimensional Markov-functional interest rate model" by Linus Kaisajuntti and Joanne Kennedy. They develop an interest rate model based on Markov-functional techniques that has similarities to an n-factor LIBOR market model. The model is suitable for pricing exotic interest rate derivative products, like targeted accrual redemption notes, and allows control of the correlations between rates as well as exploration of the impact of skews and smiles in implied volatilities.

In "A Monte Carlo pricing algorithm for autocallables that allows for stable differentiation", Thomas Alm, Bastian Harrach, Daphne Harrach and Marco Keller demonstrate how to adapt a Monte Carlo algorithm in such a way that stable sensitivities can be obtained by simple finite differences. They focus on a special kind of option known as an autocallable.

Carl Chiarella and Boda Kang focus on efficient pricing of American-type compound options. They consider the partial differential equation approach in their paper "The evaluation of American compound option prices under stochastic volatility and stochastic interest rates".

In our final paper, "A multifactor bottom-up model for pricing credit derivatives" by Lung Kwan Tsui, a stress event model, which is a bottom-up version of a specific multifactor portfolio model, is studied. Using novel insights, the loss distribution of a risky credit portfolio can be written in terms of a series expansion, enabling rapid evaluation.

I wish you very pleasant reading.


Cornelis W. Oosterlee
Delft University of Technology

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