It is a pleasure to introduce to you the December 2018 issue of The Journal of Computational Finance. As ever, it covers a broad mix of novel and imaginative computational strategies for hedging, pricing and asset allocation.
Donatien Hainaut and Franck Moraux examine the hedging of options with self- exciting market shocks; Yaxiong Zeng and Diego Klabjan demonstrate the performance of reinforcement learning for the optimization of portfolios of American options; Fabien Le Floc’h analyzes adaptive methods to evaluate the integrals that arise in standard pricing formulas for the Heston model; and J. Lars Kirkby discusses extensively a unified framework for pricing exotic and early exercise options via recursion of the value, density or characteristic function under Le´vy dynamics.
In addition, I am delighted to welcome three new members to the editorial board. Christa Cuchiero from the University of Vienna is a leading expert on the modeling of financial markets with affine and polynomial processes as well as on stochastic portfolio theory, and she has recently made groundbreaking contributions to the calibration of models with neural networks. Athena Picarelli, currently at the University of Verona, is known for her work on stochastic optimal control problems, their numerical realization and their applications to hedging, portfolio optimization and risk management. Adil Reghai, head of quantitative research for equities and commodities at Natixis, brings with him many years of experience in developing and implementing cutting-edge models within the financial industry.
Their expertise will help shape The Journal of Computational Finance in the abovementioned areas, and we particularly encourage your submissions in any of these.
I wish you an inspirational read and all the best for the coming year.
University of Oxford
This paper analyzes the efficiency of hedging strategies for stock options in the presence of jump clustering.
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
In this paper, the author describes a simple adaptive Filon method that performs better and more accurately than various popular alternatives for pricing options under the Heston model.
This paper develops a general methodology for pricing early exercise and exotic financial options by extending the recently developed PROJ method.