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Multi-Task Learning For Option Pricing

Author

Listed:
  • Yoshua Bengio
  • Joumana Ghosn
Abstract
Multi-task learning is a process used to learn domain-specific bias. It consists in simultaneously training models on different tasks derived from the same domain and forcing them to exchange domain information. This transfer of knowledge is performed by imposing constraints on the parameters defining the models and can lead to improved generalization performance. In this paper, we explore a particular multi-task learning method that forces the parameters of the models to lie on an affine manifold defined in parameter space and embedding domain information. We apply this method to the prediction of the prices of call options on the S&P index for a period of time ranging from 1987 to 1993. An analysis of variance of the results is presented that shows significant improvements of the generalization performance. L'apprentissage multi-tâches est une manière d'apprendre des particularités d'un domaine (le biais) qui comprend plusieurs tâches possibles. On entraîne simultanément plusieurs modèles, un par tâche, en imposant des contraintes sur les paramètres de manière à capturer ce qui est en commun entre les tâches, afin d'obtenir une meilleure généralisation sur chaque tâche, et pour pouvoir rapidement généraliser (avec peu d'exemples) sur une nouvelle tâche provenant du même domaine. Ici cette commonalité est définie par une variété affine dans l'espace des paramètres. Dans cet article, nous appliquons ces méthodes à la prédiction du prix d'options d'achat de l'indice S&P 500 entre 1987 et 1993. Une analyse de la variance des résultats est présentée, démontrant des améliorations significatives de la prédiction hors-échantillon.

Suggested Citation

  • Yoshua Bengio & Joumana Ghosn, 2002. "Multi-Task Learning For Option Pricing," CIRANO Working Papers 2002s-53, CIRANO.
    • Handle: RePEc:cir:cirwor:2002s-53
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    File URL: https://cirano.qc.ca/files/publications/2002s-53.pdf
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    Cited by:

    1. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.

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