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Activity Number:
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450
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 2:00 PM to 2:45 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #321653
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Title:
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Modeling Temperature-Based Financial Derivatives Through Dynamic Linear Models
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Author(s):
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David Engler* and Robert Erhardt
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Companies:
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Brigham Young University and Wake Forest University
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Keywords:
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Derivatives ;
Temperature ;
DLM ;
Bayesian
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
