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Semidefinite optimisation for global risk modelling

Author

Listed:
  • Papa Momar Ndiaye

    (RaisePartner)

  • François Oustry

    (RaisePartner)

  • Véronique Piolle

    (RaisePartner)

Abstract

One of the current challenges of risk modelling consists in building global risk models from local ones: from a set of local market risk forecasts (local covariance matrices) and cross-market correlations, a global covariance matrix preserving local market estimations and restoring a positive semidefinite matrix must be computed. Convex optimisation, taking advantage of the convex properties of dual functions, is an original and high-performing approach for such a process. In this paper, a particular semidefinite program is posed and solved with dual convex algorithms for correlation matrices in order to build a global risk model, starting from a set local market covariance, and cross-correlation. Some numerical illustrations are given.

Suggested Citation

  • Papa Momar Ndiaye & François Oustry & Véronique Piolle, 2006. "Semidefinite optimisation for global risk modelling," Journal of Asset Management, Palgrave Macmillan, vol. 7(2), pages 142-153, July.
    • Handle: RePEc:pal:assmgt:v:7:y:2006:i:2:d:10.1057_palgrave.jam.2240209
    DOI: 10.1057/palgrave.jam.2240209
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    Cited by:

    1. So, Mike K.P. & Wong, Jerry & Asai, Manabu, 2013. "Stress testing correlation matrices for risk management," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 310-322.
    2. Adrian Gepp & Geoff Harris & Bruce Vanstone, 2020. "Financial applications of semidefinite programming: a review and call for interdisciplinary research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3527-3555, December.

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