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Activity Number: 354
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #319816
Title: Modeling Temperature-Based Financial Derivatives Through Dynamic Linear Models
Author(s): David Engler* and Robert Erhardt
Companies: Brigham Young University and Wake Forest University
Keywords: Derivatives ; Temperature ; DLM ; Bayesian

Temperature derivatives are a class of financial products designed to provide financial security to those facing adverse outcomes attributable to temperature. Unlike other financial derivatives, they have no underlying tradable asset, and therefore one valuation approach is to consider only the distribution of outcomes for pricing. We adopt an approach employing Bayesian dynamic linear models (DLMs) to accurately model daily temperature data. Resultant models are then used to estimate actuarial risk measures of temperature derivatives.

Authors who are presenting talks have a * after their name.

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