I am delighted and honored to introduce this volume of The Journal of Computational Finance as its new Editor-in-Chief.
My thanks go to Kees Oosterlee, who has provided dedicated expert leadership to the journal for the past five years. I am fortunate to inherit the journal in great shape and am very pleased that he has agreed to remain on the editorial board.
Over its twenty-year history, The Journal of Computational Finance has published some of the classical papers in computational finance. The journal is uniquely placed for researchers in both industry and academia to disseminate their important findings in computational finance to a wide audience for their direct practical application.
The present issue is a snapshot of the breadth of the topics we cover, ranging from numerical analysis and algorithmic aspects, such as those developed for the computation of higher-order price sensitivities by Gilles Pagès, Olivier Pironneau and Guillaume Sall, to optimization techniques, such as those used by Louis Bhim and Reiichiro Kawai in the computation of tight bounds for option prices in regime-switching models. The journal covers topics from modeling and calibration, exemplified by Markus Falck and Mikhail Deryabin’s construction of smooth volatility surfaces via local variance gamma, to related, wider risk management issues, exhibited in Juliusz Jabłecki’s paper on model ambiguity assessed by nonparametric local volatility.
We are currently seeing an unprecedented expansion of the techniques used in the financial industry, from traditional quant research to more data-driven approaches underpinned substantially by machine learning. This is paralleled by the emergence of exciting new application areas such as FinTech, or, more specifically, cryptocurrencies and robo-advisors. The Journal of Computational Finance’s mission is to be at the forefront of any substantial developments in quantitative finance, and we are poised to lead in these exciting fields alongside our well-established other strengths.
I look forward to working with the excellent editorial board and journal team to make sure The Journal of Computational Finance remains the publication outlet of choice for the highest quality work in computational finance.
University of Oxford
Vibrato and automatic differentiation for high-order derivatives and sensitivities of financial options
This paper deals with the computation of second-order or higher Greeks of financial securities. It combines two methods, vibrato and automatic differentiation (AD), and compares these with other methods.
In this paper, the authors present a new approach to bounding financial derivative prices in regime-switching market models from both above and below.
In this paper, the authors propose a new method of constructing volatility surfaces for foreign exchange options.
This paper seeks to contribute a simple and (almost) model-free way of assessing the economic value of the Bermudan exercise right derived from a “minimal” local volatility enhanced interest rate model.