PAGES Gilles

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Affiliations
  • 2018 - 2021
    Mondes anciens et medievaux
  • 2012 - 2021
    Laboratoire de Probabilités, Statistique et Modélisation
  • 2012 - 2020
    Laboratoire de probabilités et modèles aléatoires
  • 2015 - 2017
    Université Paris 6 Pierre et Marie Curie
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2009
  • 2008
  • 2006
  • 2005
  • 2003
  • 2000
  • Risk Quantization by Magnitude and Propensity.

    Olivier p. FAUGERAS, Gilles PAGES
    2021
    We propose a novel approach in the assessment of a random risk variable $X$ by introducing magnitude-propensity risk measures $(m_X,p_X)$. This bivariate measure intends to account for the dual aspect of risk, where the magnitudes $x$ of $X$ tell how hign are the losses incurred, whereas the probabilities $P(X=x)$ reveal how often one has to expect to suffer such losses. The basic idea is to simultaneously quantify both the severity $m_X$ and the propensity $p_X$ of the real-valued risk $X$. This is to be contrasted with traditional univariate risk measures, like VaR or Expected shortfall, which typically conflate both effects. In its simplest form, $(m_X,p_X)$ is obtained by mass transportation in Wasserstein metric of the law $P^X$ of $X$ to a two-points $\{0, m_X\}$ discrete distribution with mass $p_X$ at $m_X$. The approach can also be formulated as a constrained optimal quantization problem. This allows for an informative comparison of risks on both the magnitude and propensity scales. Several examples illustrate the proposed approach.
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