The Ortec Finance scenario model combines historical data with forward looking market assumptions to produce a multivariate density forecast. The density fore-cast contains several hundred macro-economic and financial variables relevant for supporting financial decisions. Financial decisions, however, can be sensi-tive to modeling assumptions and often depend on the forecasted correlation structure.

In this paper, we describe a framework for sensitivity analyses of fi-nancial decisions with respect to the forecasted expected value, variance and, in particular, the forecasted correlation structure. For this, we use the computa-tional approach to Meucci’s entropy pooling method. When the density forecast is represented by sample points, the computational approach adjusts the density forecast by assigning weights to the sample points. This paper contributes to the current literature in two ways. First, we show how to apply the computational ap-proach in a time-dependent setting with sample paths, called scenarios, instead of sample points. Second, we present a heuristic that forces the resulting weights to be discrete. With this, the adjusted density forecast can be represented by a finite number of equally weighted scenarios.

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