Semidefinite optimisation for global risk modelling

Papa Momar Ndiaye, François Oustry and Véronique Piolle ()
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Papa Momar Ndiaye: RaisePartner
François Oustry: RaisePartner
Véronique Piolle: RaisePartner

Journal of Asset Management, 2006, vol. 7, issue 2, No 6, 142-153

Abstract: 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.

Keywords: local/global risk models; semidefinite programming; convex non-differentiable optimisation; duality (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1057/palgrave.jam.2240209

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