Pricing Multivariate European Equity Option Using Gaussians Mixture Distributions and EVT-Based Copulas

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dc.contributor.authorHassane, Abba Mallam
dc.contributor.authorDiakarya, Barro
dc.contributor.authorWendKouni, Yam´eogo
dc.date.accessioned2023-05-20T21:08:41Z
dc.date.available2023-05-20T21:08:41Z
dc.date.issued2021-09-02
dc.descriptionInternational Journal of Mathematics and Mathematical Sciences, 2021, 1-9.en_US
dc.description.abstractIn this article, we present an approach which allows taking into account the effect of extreme values in the modeling of financial asset returns and in the valorisation of associated options. Specifically, the marginal distribution of asset returns is modelled by a mixture of two Gaussian distributions. Moreover, we model the joint dependence structure of the returns using a copula function, the extremal one, which is suitable for our financial data, particularly the extreme values copulas. Applications are made on the Atos and Dassault Systems actions of the CAC40 index. Monte Carlo method is used to compute the values of some equity options such as the call on maximum, the call on minimum, the digital option, and the spreads option with the basket (Atos, Dassault systems) as underlying.en_US
dc.description.sponsorshipACE Impact: Centre for Studies, Training and Research in Social Risk Management, CEFORGRISen_US
dc.identifier.citationHassane, A. M., Diakarya, B., WendKouni, Y., & Bisso, S. (2021). Pricing Multivariate European Equity Option Using Gaussians Mixture Distributions and EVT-Based Copulas. International Journal of Mathematics and Mathematical Sciences, 2021, 1-9.en_US
dc.identifier.urihttps://doi.org/10.1155/2021/7648093
dc.identifier.urihttp://hdl.handle.net/123456789/1836
dc.language.isoenen_US
dc.publisherHindawien_US
dc.subjectSaley Bissoen_US
dc.subjectCEFORGRISen_US
dc.subjectUniversité Joseph Ki-Zerboen_US
dc.subjectBurkina-Fasoen_US
dc.titlePricing Multivariate European Equity Option Using Gaussians Mixture Distributions and EVT-Based Copulasen_US
dc.typeArticleen_US
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