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Minimizing the impact of geographical basis risk on weather derivatives
by
Moretto, Enrico
, Stefani, Silvana
, Mainini, Alessandra
, D’Aversa, Mina
, Uberti, Pierpaolo
in
Analysis
/ Climate
/ Ecological balance
/ Economic aspects
/ Hedging
/ Mathematical optimization
/ Penalty function
/ Weather
/ Weather stations
2025
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Minimizing the impact of geographical basis risk on weather derivatives
by
Moretto, Enrico
, Stefani, Silvana
, Mainini, Alessandra
, D’Aversa, Mina
, Uberti, Pierpaolo
in
Analysis
/ Climate
/ Ecological balance
/ Economic aspects
/ Hedging
/ Mathematical optimization
/ Penalty function
/ Weather
/ Weather stations
2025
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Do you wish to request the book?
Minimizing the impact of geographical basis risk on weather derivatives
by
Moretto, Enrico
, Stefani, Silvana
, Mainini, Alessandra
, D’Aversa, Mina
, Uberti, Pierpaolo
in
Analysis
/ Climate
/ Ecological balance
/ Economic aspects
/ Hedging
/ Mathematical optimization
/ Penalty function
/ Weather
/ Weather stations
2025
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Minimizing the impact of geographical basis risk on weather derivatives
Journal Article
Minimizing the impact of geographical basis risk on weather derivatives
2025
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Overview
In the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.
Publisher
Springer,Springer Nature B.V
Subject
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