Time flies, dear Reader. In 2013, I agreed to act as the editor-in-chief of The Journal of Computational Finance for five years. That seems like only yesterday, but here we are, five years later, and I believe it is now time to pass on the editorship.
I am happy to announce that Professor Christoph Reisinger of Oxford University has agreed to take over the highly interesting task of directing The Journal of Computational Finance. Christoph is a very well-known expert in the field of computational finance, having made important contributions to numerical partial differential equations and Monte Carlo methods as well as modeling with stochastic (partial) differential equations. I wish Christoph all the best and lots of excellent papers! I am also happy to remain connected to the journal as an associate editor.
In these last five years, The Journal of Computational Finance has adopted a more professional approach to handling manuscripts, by means of an electronic portal via which we receive incoming papers. This has been an important step forward for the journal, I think. Moreover, the journal’s twentieth anniversary was celebrated with the publication of its twentieth volume, two years ago. The first issue of that volume contained research by well-known authors who had contributed seminal papers to The Journal of Computational Finance over its first twenty years in print. I recently learned in a friendly email from Peter Field, who is now at Field Gibson Media Ltd, that the journal was the brainchild of Domingo Tavella. In 1997, Peter – as publisher – launched The Journal of Computational Finance: the first academic journal to be spun off from Risk magazine, which he had started ten years earlier. Peter sold the Risk Waters Group in 2003. He was proud of twenty years of The Journal of Computational Finance, and so am I. Now the journal is in the hands of Infopro Digital – another change during these last five years – and we now publish five issues per year instead of four.
Let us move on to introducing the June 2018 issue of The Journal of Computational Finance, which contains four very different papers.
Our first paper is “Investment opportunities forecasting: a genetic programming- based dynamic portfolio trading system under a directional-change framework” by Monira Essa Aloud. This paper falls into the category of artificial intelligence techniques for forecasting tasks in financial markets. Genetic programming is proposed as a technique for automatically generating short-term trading rules by means of technical indicators and fundamental parameters. An agent-based simulation framework for index trading is explained in the paper.
“Kriging metamodels and experimental design for Bermudan option pricing” by Mike Ludkovski is the issue’s second paper. In it, the regression Monte Carlo framework, as proposed by Longstaff and Schwartz in 2001, is enhanced with stochastic Kriging to better fit the continuation values. Kriging is a well-known statistical technique, and in this context it offers a quantification of the quality of the regression approximation within optimal stopping problems. Moreover, a framework of improved experimental design based on space-filling and adaptivity is explained. The paper comes with several numerical experiments, confirming the robust and efficient solution technique.
Sergio De Diego, Eva Ferreira and Eulalia Nualart have contributed to the issue with “Importance sampling applied to Greeks for jump–diffusion models with stochastic volatility”. Their paper, the third in this issue, develops a variance reduction technique in the context of the so-called Robbins–Monro algorithm for option pricing under jump–diffusion with stochastic volatility. The main technique behind importance sampling is changing the drift term to reduce the variance of a sample. The idea is to determine an optimal drift in the Girsanov transform, under which the variance of the estimate is minimal. Numerical results for the Black–Scholes and Heston models with jumps as well as the Barndorff–Nielsen–Shephard model confirm the efficiency improvement.
Our fourth paper, “Importance sampling for jump–diffusions via cross-entropy”, is by Rebecca Rieke, Weifeng Sun and Hui Wang. This is another paper on the importance sampling variance reduction technique being applied to a class of jump–diffusion processes. An efficient and easy-to-implement importance sampling scheme is proposed, based on the method of cross-entropy, which is combined with an expectation–maximization algorithm. The sampling distributions are selected from the family of exponentially tilted distributions and their mixtures. A variety of path-dependent options are simulated with this technique to illustrate its fine performance.
Finally, I would like to thank the very friendly people in the editorial office in London, who have helped me with The Journal of Computational Finance whenever necessary. At present, it is Ciara Smith and Sarah Campbell at Infopro Digital, but previously I had Dawn Hunter, Carolyn Moclair and Jade Mitchell helping me out. Nada Mitrovic also helped me with the journal at the CWI – Center for Mathematics & Computer Science in Amsterdam. This is warmly acknowledged!
Let me also thank all the associate editors for their invaluable help with and support of the journal over the years. And, last but certainly not least, I thank the authors and readers. Your continued interest in this fascinating journal is hugely appreciated.
I wish you very enjoyable reading of this June 2018 issue of The Journal of Computational Finance.
Cornelis W. Oosterlee
CWI – Dutch Center for Mathematics and Computer Science, Amsterdam
Investment opportunities forecasting: a genetic programming-based dynamic portfolio trading system under a directional-change framework
This paper presents an autonomous effective trading system devoted to the support of decision-making processes in the financial market domain.
This paper investigates two new strategies for the numerical solution of optimal stopping problems in the regression Monte Carlo (RMC) framework.
In this paper, the authors develop a procedure to reduce the variance when numerically computing the Greeks obtained via Malliavin calculus for jump–diffusion models with stochastic volatility.
This paper develops efficient importance sampling schemes for a class of jump–diffusion processes that are commonly used for modeling stock prices.