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A sparse grid approach to balance sheet risk measurement

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Listed:
  • Cyril B'en'ezet
  • J'er'emie Bonnefoy
  • Jean-Franc{c}ois Chassagneux
  • Shuoqing Deng
  • Camilo Garcia Trillos
  • Lionel Len^otre

Abstract

In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distribution. For the pricing and hedging model, we chose a classical Black & Scholes model with a stochastic interest rate following a Hull & White model. The risk management model describing the evolution of the parameters of the pricing and hedging model is a Gaussian model. The new numerical method is compared with the traditional nested simulation approach. We review the convergence of both methods to estimate the risk indicators under consideration. Finally, we provide numerical results showing that the sparse grid approach is extremely competitive for models with moderate dimension.

Suggested Citation

  • Cyril B'en'ezet & J'er'emie Bonnefoy & Jean-Franc{c}ois Chassagneux & Shuoqing Deng & Camilo Garcia Trillos & Lionel Len^otre, 2018. "A sparse grid approach to balance sheet risk measurement," Papers 1811.08706, arXiv.org.
    • Handle: RePEc:arx:papers:1811.08706
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    References listed on IDEAS

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    1. Michael B. Gordy & Sandeep Juneja, 2010. "Nested Simulation in Portfolio Risk Measurement," Management Science, INFORMS, vol. 56(10), pages 1833-1848, October.
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    Cited by:

    1. Sripad Motiram & Vamsi Vakulabharanam, 2020. "Intra-City Inequalities, Neighborhoods and Economic Development," Working Papers 2020-01, University of Massachusetts Boston, Economics Department.

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