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H 1 CVaR hedging using quantization based stochastic approximation algorithm

Auteurs : Pagès, Gilles (Auteur de la Conférence)
CIRM (Editeur )

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    Résumé : We investigate a method based on risk minimization to hedge observable but non-tradable source of risk on financial or energy markets. The optimal portfolio strategy is obtained by minimizing dynamically the Conditional Value-at-Risk (CVaR) using three main tools: a stochastic approximation algorithm, optimal quantization and variance reduction techniques (importance sampling (IS) and linear control variable (LCV)) as the quantities of interest are naturally related to rare events. We illustrate our approach by considering several portfolios in connection with energy markets.

    Keywords : VaR, CVaR, Stochastic Approximation, Robbins-Monro algorithm, Quantification

    Codes MSC :
    62L20 - Stochastic approximation
    91B30 - Risk theory, insurance
    91G70 - Statistical methods in mathematical finance, econometrics

      Informations sur la Vidéo

      Langue : Anglais
      Date de publication : 11/09/14
      Date de captation : 09/09/14
      Collection : Research talks ; Analysis and its Applications
      Format : quicktime ; audio/x-aac
      Durée : 00:34:13
      Domaine : Analysis and its Applications
      Audience : Chercheurs ; Doctorants , Post - Doctorants
      Download : https://videos.cirm-math.fr/2014-09-09_Pages.mp4

    Informations sur la rencontre

    Nom de la rencontre : Advances in stochastic analysis for risk modeling / Analyse stochastique pour la modélisation des risques
    Organisateurs de la rencontre : Bouchard, Bruno ; Chassagneux, Jean-François ; Elie, Romuald ; Réveillac, Anthony ; Soner, H. Mete
    Dates : 08/09/14 - 12/09/14
    Année de la rencontre : 2014

    Citation Data

    DOI : 10.24350/CIRM.V.18600003
    Cite this video as: Pagès, Gilles (2014). CVaR hedging using quantization based stochastic approximation algorithm. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18600003
    URI : http://dx.doi.org/10.24350/CIRM.V.18600003


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