University of Oxford
Carlos Vázquez Cendón
University of A Coruña
This volume of The Journal of Computational Finance is the second part of a special issue of peer-reviewed articles related to works presented at the Third International Conference on Computational Finance (ICCF2019). Following successful events in Greenwich (2015) and Lisbon (2017), this third incarnation, organized by the University of A Coruña, brought around 120 participants from 25 countries to A Coruña (Spain) from July 8 to 11, 2019.
ICCF2019 included plenary talks from academic researchers and industry professionals, minisymposia and contributed talks. The presentations covered diverse topics of current interest on the modeling and computational aspects of financial problems. The program included an industrial roundtable on hot topics in the financial industry.
Among the attendees were an encouraging number of young researchers. Although the competition was tight due to the high standard of entries, the Journal of Computational Finance Young Researcher Award for the best work submitted by a researcher within five years of completing their PhD was ultimately presented to Anastasia Borovykh and Beatriz Salvador.
The Organizing Committee of ICCF2019 is grateful to all participants for their stimulating scientific discussions, to the Scientific Committee for its valuable input on the scientific program, to the local Organizing Committee coordinated by Iñigo Arregui for its enthusiasm and the smooth running of the conference, to the Fundación Barrié and Afundación for hosting ICCF2019 in their buildings, and, last but not least, to all of the event’s sponsors: the Xunta de Galicia government through the “Axudas de Consolidacion a Grupos de referencia competitiva” program; the Spanish Research Agency through the Strategic Mathematics Network (REM); the Galician Singular Research Center CITIC; the Technological Institute of Industrial Mathematics (ITMATI); the European Consortium of Mathematics and Industry (ECMI); the European Commission’s Horizon 2020 Programme; the Spanish bank Abanca; the University of A Coruña; and our publisher, Risk.net.
The papers in this special issue of The Journal of Computational Finance follow on from those published in Volume 24, Issue 3. In our first paper, “Gradient boosting for quantitative finance”, Jesse Davis, Laurens Devos, Sofie Reyners and Wim Schoutens demonstrate how computationally expensive derivative pricing problems can be accelerated dramatically by training gradient boosted regression trees.
“Penalty methods for bilateral XVA pricing in European and American contingent claims by a partial differential equation model”, the second in the issue, finds Yuwei Chen and Christina C. Christara discussing the efficient iterative solution of a nonlinear partial differential equation from XVA modeling by exploiting similarities with established penalty methods.
Álvaro Leitao, J. Lars Kirkby and Luis Ortiz-Gracia consider a Markov chain approximation of the Heston model in “The CTMC–Heston model: calibration and exotic option pricing with SWIFT”, this issue’s third paper. The authors demonstrate the advantageous numerical properties of their approach for the pricing of exotic options.
In our final paper, “Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options”, Alan Bain, Matthieu Mariapragassam and Christoph Reisinger, who is one of the editors of The Journal of Computational Finance, present a generalization of the Markovian projection approach for volatility models, which is well established for vanilla options, to options with barrier features. They also give worked-out applications in the foreign exchange market.
We wish you an inspirational read.
Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options
In this paper, the authors consider a large class of continuous semi-martingale models and propose a generic framework for their simultaneous calibration to vanilla and no-touch options.
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
Penalty methods for bilateral XVA pricing in European and American contingent claims by a partial differential equation model
Under some assumptions, the valuation of financial derivatives, including a value adjustment to account for default risk (the so-called XVA), gives rise to a nonlinear partial differential equation (PDE). The authors propose numerical methods for…
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.