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Rearrangement Algorithm in Risk Aggregation

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thesis
posted on 24.05.2021, 10:18 by Alex Thomas
Sometimes there’s no closed-form analytical solutions for the risk measure of aggregate losses representing, say, a company’s losses in each country or city it operates in, a portfolio of losses subdivided by investment, or claims made by clients to an insurance company. Assuming there’s enough data to assign a distribution to those losses, we examine the Rearrangement Algorithm’s ability to numerically compute the Expected Shortfall and Exponential Premium Principle/Entropic Risk Measure of aggregate losses. A more efficient discretization scheme is introduced and the algorithm is extended to the Entropic Risk Measure which turns out to have a smaller uncertainty spread than the Expected Shortfall at least for the cases that we examined.

History

Language

eng

Degree

Master of Science

Program

Applied Mathematics

Granting Institution

Ryerson University

LAC Thesis Type

Thesis