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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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DTSTART:20250309T080000
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DTSTART;TZID=America/Chicago:20250425T120000
DTEND;TZID=America/Chicago:20250425T130000
DTSTAMP:20260521T010252
CREATED:20241216T174606Z
LAST-MODIFIED:20250425T213005Z
UID:1694-1745582400-1745586000@www.math.ttu.edu
SUMMARY:Drought parametric insurances by a Two-Step machine learning approach under climate change scenarios
DESCRIPTION:Speaker: Dr. Hirbod Assa\, Founding team | Quantitative researcher\, Edge Technologies; Director and founder\, Model Library Ltd. \nAbstract: In this paper\, we utilize data from the IPCC to develop a predictive model for the Palmer Drought Severity Index (PDSI). Our approach involves a two-step modeling process: initially applying a random forest regression\, followed by a linear regression correction\, achieving a forecasting accuracy exceeding 94%. This method aims to predict the drought index using a minimal set of climate indices\, with projections extending to the year 2100 based on CORDEX CMIP5 models. The analysis focuses on selected U.S. states\, assessing the impacts of different climate scenarios and climate change under various RCP pathways. On that basis we design a parametric insurance on drought index. If time permits\, we will also explore the implications of these drought projections on supply chain risks in the beef commodity market.
URL:https://www.math.ttu.edu/mathematicalfinance/event/title-to-be-provided/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/hirbod.jpg
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