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This paper addresses the ``curse of dimensionality'' in the loss valuation ofcredit risk models. A dimension reduction methodology based on the Bayesianfilter and smoother is proposed. This methodology is designed to achieve a fastand accurate loss valuation algorithm in credit risk modelling, but it can alsobe extended to valuation models of other risk types. The proposed methodologyis generic, robust and can easily be implemented. Moreover, the accuracy of theproposed methodology in the estimation of expected loss and value-at-risk isillustrated by numerical experiments. The results suggest that, compared to thecurrently most used PCA approach, the proposed methodology provides moreaccurate estimation of expected loss and value-at-risk of a loss distribution.

A dimension reduction approach for loss valuation in credit risk modelling