Enhanced Scenario Analysis

Abstract

Investors have long relied on scenario analysis as an alternative to mean–variance analysis to help them construct portfolios. Even though mean–variance analysis accounts for all potential scenarios, many investors find it difficult to implement because it requires them to specify statistical features of asset classes that are often unintuitive and difficult to estimate. Scenario analysis, by contrast, requires only that investors specify a small set of potential outcomes as projections of economic variables and assign probabilities to their occurrence. It is, therefore, more intuitive than mean–variance analysis, but it is highly subjective. In this article, the authors propose to replace the subjective elements of scenario analysis with a robust statistical process. They use a multivariate measure of statistical distance to estimate probabilities of prospective scenarios. Next, they construct portfolios that maximize utility for investors with different risk preferences. Last, the authors introduce a procedure for minimally modifying scenarios to render them consistent with prespecified views about their probabilities of occurrence.

TOPICS: Portfolio theory, portfolio construction

Key Findings

• • The authors use a multivariate measure of statistical distance to estimate probabilities of prospective scenarios.

• • They construct portfolios that maximize utility for investors with different risk preferences.

• • The authors introduce a procedure for minimally modifying scenarios to render them consistent with one’s prespecified views about their probabilities of occurrence.