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Incorporating climate change projections into risk measures of index-based insurance. (English) Zbl 1461.91248

Summary: We use regional climate model projections to quantify long-term projected changes to risk measures for an example set of temperature index-based insurance products in California. This region is a major agricultural producer for the United States and the world. The climate model projections are an ensemble of six regional climate model runs obtained from the North American Regional Climate Change Assessment Program. Hindcasts for the period of 1971–2000 are compared to historical observed temperature data for bias and variance corrections. Adjusted future model projections are used to estimate distributions of cooling degree days for 2041–2070, which are then used to estimate risk measures for index-based insurance products to demonstrate the scale of changes that climate models project through mid-century in actuarial terms. More broadly, this article provides an illustration of the use of climate data products to explore actuarial risks.

MSC:

91G05 Actuarial mathematics
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[1] Ahmed, K. F.; Wang, G.; Silander, J.; Wilson, A. M.; Allen, J. M.; Horton, R.; Anyah., R., Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. Northeast, Global and Planetary Change, 100, 320-32 (2013)
[2] Alaton, P.; Djehiche, B.; Stillberger., D., On modelling and pricing weather derivatives, Applied Mathematical Finance, 9, 1, 1-20 (2002) · Zbl 1013.91036
[3] Barnett, B. J.; Mahul., O., Weather index insurance for agriculture and rural areas in lower-income countries, American Journal of Agricultural Economics, 89, 5, 1241-47 (2007)
[4] Campbell, S. D.; Diebold., F. X., Weather forecasting for weather derivatives, Journal of the American Statistical Association, 100, 469, 6-16 (2005) · Zbl 1117.62305
[5] Carriquiry, M. A.; Osgood., D. E., Index insurance, probabilistic climate forecasts, and production, Journal of Risk and Insurance, 79, 1, 287-300 (2012)
[6] Chang, C. W.; Chang, J. S.; Lim., K. G., Global warming, extreme weather events, and forecasting tropical cyclones: A market-based forward-looking approach, ASTIN Bulletin, 42, 1, 77-101 (2012) · Zbl 1277.91143
[7] Chantarat, S.; Mude, A. G.; Barrett, C. B.; Carter., M. R., Designing index-based livestock insurance for managing asset risk in northern Kenya, Journal of Risk and Insurance, 80, 1, 205-37 (2013)
[8] Christensen, J. H.; Boberg, F.; Christensen, O. B.; Lucas-Picher., P., On the need for bias correction of regional climate change projections of temperature and precipitation, Geophysical Research Letters, 35, 20 (2008)
[9] Collier, B.; Skees, J.; Barnett., B., Weather index insurance and climate change: Opportunities and challenges in lower income countries, The Geneva Papers on Risk and Insurance-Issues and Practice, 34, 3, 401-24 (2009)
[10] Deng, X.; Barnett, B. J.; Vedenov, D. V.; West., J. W., Hedging dairy production losses using weather-based index insurance, Agricultural Economics, 36, 2, 271-80 (2007)
[11] Dupuis, D. J., Modeling waves of extreme temperature: The changing tails of four cities, Journal of the American Statistical Association, 107, 497, 24-39 (2012) · Zbl 1261.62104
[12] Dupuis, D. J., A model for nighttime minimum temperatures, Journal of Climate, 27, 19, 7207-29 (2014)
[13] Erhardt, R., Mid-twenty-first-century projected trends in North American heating and cooling degree days, Environmetrics, 26, 2, 133-44 (2015)
[14] Erhardt, R. (2017)
[15] Erhardt, R.; Engler., D., An extension of spatial dependence models for estimating short-term temperature portfolio risk, North American Actuarial Journal, 22, 3, 473-90 (2018) · Zbl 1416.91174
[16] Erhardt, R.; von Burg., R. (2018)
[17] Fischer, T.; Su, B.; Luo, Y.; Scholten., T., Probability distribution of precipitation extremes for weather index-based insurance in the Zhujiang River basin, South China, Journal of Hydrometeorology, 13, 3, 1023-37 (2012)
[18] Fleege, T. A.; Richards, R. J.; Manfredo, M. R.; Sanders, D. R., The performance of weather derivatives in managing risks of specialty crops, Paper presented at NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management (2004)
[19] Gent, P. R.; Danabasoglu, G.; Donner, L. J.; Holland, M. M.; Hunke, E. C.; Jayne, S. R.; Lawrence, D. M.; Neale, R. B.; Rasch, P. J.; Vertenstein, M., The community climate system model version 4, Journal of Climate, 24, 19, 4973-91 (2011)
[20] Hansen, J.; Ruedy, R.; Sato, M.; Lo., K., Global surface temperature change, Reviews of Geophysics, 48, 4 (2010)
[21] Hansen, J.; Sato, M.; Ruedy., R., Perception of climate change, Proceedings of the National Academy of Sciences, 109, 37, E2415-23 (2012)
[22] Jewson, S.; Brix., A., Weather derivative valuation: The meteorological, statistical, financial and mathematical foundations (2005), Cambridge University Press
[23] Linnerooth-Bayer, J.; Mechler., R., Insurance for assisting adaptation to climate change in developing countries: A proposed strategy, Climate Policy, 6, 6, 621-36 (2006)
[24] Mearns, L. O.; Arritt, R.; Boer, G.; Caya, D.; Duffy, P.; Giorgi, F.; Gutowski, W.; Held, I.; Jones, R.; Laprise, R., NARCCAP, North American Regional Climate Change Assessment Program, Paper presented at 16th Conference on Climate Variability and Change (2005)
[25] Mearns, L. O.; McGinnis, S.; Arritt, R.; Biner, S.; Duffy, P.; Gutowski, W.; Held, I.; Jones, R.; Leung, R.; Nunes, A., The North American Regional Climate Change Assessment Program dataset (2007), Boulder, CO: National Center for Atmospheric Research Earth System Grid Data Portal, Boulder, CO
[26] Mills, E., Insurance in a climate of change, Science, 309, 5737, 1040-44 (2005)
[27] Munich Climate Insurance Initiative (2013)
[28] Nakicenovic, N.; Alcamo, J.; Grubler, A.; Riahi, K.; Roehrl, R.; Rogner, H.-H.; Victor, N., Special report on emissions scenarios (SRES), a special report of Working Group III of the Intergovernmental Panel on Climate Change (2002), Cambridge University Press
[29] Pachauri, R. K.; Allen, M. R.; Barros, V. R.; Broome, J.; Cramer, W.; Christ, R.; Church, J. A.; Clarke, L.; Dahe, Q.; Dasgupta, P. (2014)
[30] (2019)
[31] Silverman, B., Density estimation for statistics and data analysis (1986) · Zbl 0617.62042
[32] Surminski, S.; Bouwer, L. M.; Linnerooth-Bayer., J., How insurance can support climate resilience, Nature Climate Change, 6, 4, 333-34 (2016)
[33] Turvey, C. G., The pricing of degree-day weather options, Agricultural Finance Review, 65, 1, 59-85 (2005)
[34] Turvey, C. G.; Mclaurin., M. K., Applicability of the Normalized Difference Vegetation Index (NDVI) in index-based crop insurance design, Weather, Climate, and Society, 4, 4, 271-84 (2012)
[35] Zeng, L., Weather derivatives and weather insurance: Concept, application, and analysis, Bulletin of the American Meteorological Society, 81, 9, 2075-82 (2000)
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