On the aggregation of local risk models for global risk management

Greg Anderson, Lisa Goldberg, Alec N. Kercheval, Guy Miller, Kathy Sorge

Abstract

ABSTRACT

Given a collection of single-market covariance matrix forecasts for different markets, we describe how to embed them into a global forecast of total risk. We do this by starting with any global covariance matrix forecast that contains information about cross-market correlations and revise it to agree with the pre-specified sub-market matrices, preserving the requirement that a covariance matrix be positive semi-definite. We characterize the ways this can be done and address the resulting numerical optimization problem.