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Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery

Cazenave, Tristan; Ben Hamida, Sana (2015), Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery, dans IEEE, 2015 IEEE Symposium Series on Computational Intelligence, IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 726-733. 10.1109/SSCI.2015.110

Type
Communication / Conférence
Date
2015
Titre du colloque
2015 IEEE Symposium Series on Computational Intelligence
Date du colloque
2015-12
Ville du colloque
Cape Town
Pays du colloque
South Africa
Titre de l'ouvrage
2015 IEEE Symposium Series on Computational Intelligence
Auteurs de l’ouvrage
IEEE
Éditeur
IEEE - Institute of Electrical and Electronics Engineers
Ville d’édition
Piscataway, NJ
Isbn
978-1-4799-7560-0
Pages
726-733
Identifiant publication
Métadonnées
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Auteur(s)
Cazenave, Tristan
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ben Hamida, Sana cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Résumé (EN)
We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.
Mots-clés
Monte Carlo methods; Forecasting; Genetic programming; Time series analysis