As the summer draws to a close, we look back at an exciting conference season that has showcased recent developments in quantitative finance. One of the field’s flagship events, with strong engagement from The Journal of Computational Finance, was the biennial International Conference on Computational Finance (ICCF), held this year in A Corun˜ a, Spain (July 8–12). From an impressive lineup of excellent presenters, around twenty were considered to receive the new JCF Young Researcher Award. A jury of journal editors and other leaders in the field ultimately split the award between two recent PhD graduates: Anastasia Borovykh was awarded a prize for her work on novel neural network techniques in financial time series forecasting, while Beatriz Salvador was recognized for her rigorous analysis and computation of partial differential equation models in counterparty credit risk. I congratulate them most warmly for their fantastic achievements, and I look forward to following their future research. Other notable themes of the conference included innovative calibration approaches and groundbreaking learning methods for the solution of high-dimensional problems. We look forward to publishing a special issue on research highlights from the conference next year.
Coincidentally, in this issue we have contributions from Carlos Vázquez, who organized this year’s ICCF, and Matthias Ehrhardt, who will arrange the 2021 ICCF in Wuppertal, Germany (May 24–28).
Specifically, in the issue’s first paper, “A new approach to the quantification of model risk for practitioners”, Zuzana Krajčovičová, Pedro Pablo Pérez-Velasco and Carlos Vázquez introduce a new model risk measure based on Riemannian geometry, while in our second paper, “The two-dimensional tree–grid method”, Igor Kossaczký, Matthias Ehrhardt and Michael Günther construct and analyze a new hybrid scheme for the approximation of stochastic control problems with two state variables. In “The standard market risk model of the Swiss solvency test: an analytic solution”, our third paper, Andras Niedermayer proposes a fast Fourier transform approach to calculating the regulatory target capital. Finally, in “Path independence of exotic options and convergence of binomial approximations”, Guillaume Leduc and Kenneth J. Palmer speed up the computation of options with certain path-dependent payoffs by using path-independent equivalents.
I wish you inspirational reading.
University of Oxford
This paper's aim is twofold: to introduce a mathematical framework that is sufficiently general and sound to cover the main areas of model risk, and to illustrate how a practitioner can identify the relevant abstract concepts and put them to work.
In this paper, the authors introduce a novel, explicit, wide-stencil, two-dimensional (2D) tree–grid method for solving stochastic control problems (SCPs) with two space dimensions and one time dimension, or, equivalently, the corresponding Hamilton…
This paper derives an alternative fast Fourier transform-based computational approach for calculating the target capital of the SST that is more than 600 times faster than a Monte Carlo simulation.
In this paper, the authors analyse the convergence of tree methods for pricing barrier and lookback options.