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Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorBen Hamida, Sana
HAL ID: 177299
ORCID: 0000-0003-4202-613X
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorCazenave, Tristan
HAL ID: 743184
dc.date.accessioned2020-05-12T11:37:40Z
dc.date.available2020-05-12T11:37:40Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20718
dc.language.isoenen
dc.subjectGenetic Programmingen
dc.subject.ddc005en
dc.titleNested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatilityen
dc.typeDocument de travail / Working paper
dc.description.abstractenWe are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learning from financial time series to generate nonlinear 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.en
dc.publisher.namePreprint Lamsadeen
dc.publisher.cityParisen
dc.relation.ispartofseriestitlePreprint Lamsadeen
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.identifier.citationdate2020
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2020-05-12T11:34:57Z
hal.author.functionaut
hal.author.functionaut


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