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.
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