Special Issue "Modern Numerical Techniques and Machine-Learning in Pricing and Risk Management"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Prof. Cornelis W. Oosterlee

CWI – Center for Mathematics & Computer Science, Amsterdam, and Delft University of Technology, Delft, the Netherlands
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Guest Editor
Dr. Lech A. Grzelak

Delft University of Technology, Delft and Rabobank Group, Utrecht, the Netherlands
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Special Issue Information

Dear Colleagues,

In present day financial practice, we need to model and price the impact of a counterparty going bankrupt. In modern risk management, different valuation adjustments, commonly known as “xVA” (where “VA" stands for valuation adjustment and the "x" means “any letter”, where each letter stands for a different VA component), are added to the fair value of a financial derivative. Accurate pricing and hedging of these VAs is of a high importance and requires sophisticated models and numerical techniques.

At the same time, we observe a high interest in financial machine-learning, both at the level of pricing and price prediction, as on the level of risk management (“learning the client, learning the creditworthiness, etc.”).

We would like to connect both of these recent themes in this Special Issue, which will publish high-quality research papers on machine-learning in computational finance, and on advanced risk management. Combinations of these themes are especially interesting.

Prof. Cornelis W. Oosterlee
Dr. Lech A. Grzelak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Numerical methods in computational finance
  • Risk management and derivative valuation 
  • XVA, CVA, FVA, MVA, etc. 
  • Machine-learning in computational finance

Published Papers (1 paper)

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Research

Open AccessArticle Pricing Options and Computing Implied Volatilities using Neural Networks
Received: 8 January 2019 / Revised: 3 February 2019 / Accepted: 6 February 2019 / Published: 9 February 2019
PDF Full-text (926 KB)
Abstract
This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized [...] Read more.
This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent’s iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly. Full article
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