KRIGING METHODS FOR MODELING SPATIAL BASIS RISK IN WEATHER INDEX INSURANCES: A TECHNICAL NOTE

Yiping Guo () and Johnny Siu-Hang Li
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Yiping Guo: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave West, Waterloo, Ontario N2L3G1, Canada
Johnny Siu-Hang Li: Department of Finance, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

International Journal of Theoretical and Applied Finance (IJTAF), 2024, vol. 27, issue 01, 1-24

Abstract: The use of weather index insurances is subject to spatial basis risk, which arises from the fact that the location of the user’s risk exposure is not the same as the location of any of the weather stations where an index can be measured. To gauge the effectiveness of weather index insurances, spatial interpolation techniques such as kriging can be adopted to estimate the relevant weather index from observations taken at nearby locations. In this paper, we study the performance of various statistical methods, ranging from simple nearest neighbor to more advanced trans-Gaussian kriging, in spatial interpolations of daily precipitations with data obtained from the US National Oceanic and Atmospheric Administration. We also investigate how spatial interpolations should be implemented in practice when the insurance is linked to popular weather indexes including annual consecutive dry days (CDD) and maximum five-day precipitation in one month (MFP). It is found that although spatially interpolating the raw weather variables on a daily basis is more sophisticated and computationally demanding, it does not necessarily yield superior results compared to direct interpolations of CDD/MFP on a yearly/monthly basis. This intriguing outcome can be explained by the statistical properties of the weather indexes and the underlying weather variables.

Keywords: Actuaries climate index; kriging; precipitation indexes; spatial basis risk; weather index insurances (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219024923500346

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