Managing Weather Risk with a Neural Network-Based Index Insurance
References
- (2019) Advances in weather prediction. Science 363(6425):342–344.Crossref, Google Scholar
- (2021) Robo-advising: Learning investors’ risk preferences via portfolio choices. J. Financial Econom. 19(2):369–392.Crossref, Google Scholar
- (2021) Price index insurances in the agriculture markets. North Amer. Actuary J. 25(2):286–311.Crossref, Google Scholar
- (2022) Machine learning vs. economic restrictions: Evidence from stock return predictability. Management Sci. 69(5):2587–2619.Link, Google Scholar
- (2020) On the empirical validity of cumulative prospect theory: Experimental evidence of rank-independent probability weighting. Econometrica 88(4):1363–1409.Crossref, Google Scholar
- (2021) Bond risk premia with machine learning. Rev. Financial Stud. 34(2):1046–1089.Crossref, Google Scholar
- (2019) Ambiguity aversion decreases the impact of partial insurance: Evidence from African farmers. J. Eur. Econom. Assoc. 17(5):1428–1469.Crossref, Google Scholar
- (2021) Forest through the trees: Building cross-sections of stock returns, Working paper, Stanford University, Stanford, CA.Google Scholar
- (2020) Subsidy policies and insurance demand. Amer. Econom. Rev. 110(8):2422–2453.Crossref, Google Scholar
- Capponi A, Lehalle CA, eds. (2023) Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2022) Personalized robo-advising: Enhancing investment through client interaction. Management Sci. 68(4):2485–2512.Link, Google Scholar
- (2018) Time vs. state in insurance: Experimental evidence from contract farming in Kenya. Amer. Econom. Rev. 108(12):3778–3813.Crossref, Google Scholar
- (2020) Demand heterogeneity for index-based insurance: The case for flexible products. J. Developement Econom. 146:102515.Crossref, Google Scholar
- (1990) Acreage decisions under risk: The case of corn and soybeans. Amer. J. Agricultural Econom. 72(3):529–538.Crossref, Google Scholar
- (2023) Deep learning in asset pricing. Management Sci., ePub ahead of print February 20, https://doi.org/10.1287/mnsc.2023.4695.Google Scholar
- (2016) A theory of rational demand for index insurance. Amer. Econom. J. Microeconom. 8(1):283–306.Crossref, Google Scholar
- (2014) Dynamics of demand for index insurance: Evidence from a long-run field experiment. Amer. Econom. Rev. 104(5):284–290.Crossref, Google Scholar
- (2013) Barriers to household risk management: Evidence from India. Amer. Econom. J. Appl. Econom. 5(1):104–135.Crossref, Google Scholar
- (2020) Textual factors: A scalable, interpretable, and data-driven approach to analyzing unstructured information. Working paper, Cornell University, Ithaca, NY.Google Scholar
- (2022) Asset pricing with panel trees under global split criteria. Working paper, Cornell University, Ithaca, NY.Google Scholar
- (2021a) AlphaPortfolio: Direct construction through deep reinforcement learning and interpretable AI. Working Paper, Cornell University, Ithaca, NY.Google Scholar
- (2021b) Deep sequence modeling: Development and applications in asset pricing. J. Financial Data Sci. 3(1):28–42.Crossref, Google Scholar
- (2018) Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environment. Res. Lett. 13(11):114003.Crossref, Google Scholar
- (2004) The basis risk of catastrophic-loss index securities. J. Financial Econom. 71(1):77–111.Crossref, Google Scholar
- (2007) Is there a viable market for area-based crop insurance? Amer. J. Agricultural Econom. 89(2):508–519.Crossref, Google Scholar
- (1983) Optimal insurance in incomplete markets. J. Political Econom. 91(6):1045–1054.Crossref, Google Scholar
- (2019) Hedging climate change news. Rev. Financial Stud. 33(3):1184–1216.Crossref, Google Scholar
- (2020) Taming the factor zoo: A test of new factors. J. Finance 75(3):1327–1370.Crossref, Google Scholar
- (2021) Deep learning in characteristics-sorted factor models. Working paper, University of Chicago, Chicago, IL.Google Scholar
- (2012) The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather: Comment. Amer. Econom. Rev. 102(7):3749–3760.Crossref, Google Scholar
- (2011) Marketing complex financial products in emerging markets: Evidence from rainfall insurance in India. J. Marketing Res. 48(SPL):S150–S162.Crossref, Google Scholar
- (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
- (2021) Autoencoder asset pricing models. J. Econometrics 222(1):429–450.Crossref, Google Scholar
- (2011) Relaxing heteroscedasticity assumptions in area-yield crop insurance rating. Amer. J. Agricultural Econom. 93(3):707–717.Crossref, Google Scholar
- (2020) The insurance is the lemon: Failing to index contracts. J. Finance 75(1):463–506.Crossref, Google Scholar
- (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin).Crossref, Google Scholar
- (2020) Climate finance. Rev. Financial Stud. 33(3):1011–1023.Crossref, Google Scholar
- (2022) Machine-learning-based return predictors and the spanning controversy in macro-finance. Management Sci. 69(3):1780–1804.Google Scholar
- Illinois Department of Agriculture (2020) Cover crops premium discount program. Technical report, Illinois Department of Agriculture, Springfield, IL.Google Scholar
- (2016) Index insurance quality and basis risk: Evidence from northern Kenya. Amer. J. Agricultural Econom. 98(5):1450–1469.Crossref, Google Scholar
- (2016) Influence of extreme weather disasters on global rop production. Nature 529:84–88.Crossref, Google Scholar
- (2021) Selecting mutual funds from the stocks they hold: A machine learning approach, Working paper, Georgetown University, Washington, DC.Google Scholar
- (2019) Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biology 25(7):2325–2337.Crossref, Google Scholar
- (1984) Linear and Nonlinear Programming, 5th ed. (Springer, Berlin).Google Scholar
- (2007) Managing Agricultural Risk at the Country Level: The Case of Index-Based Livestock Insurance in Mongolia (The World Bank).Google Scholar
- (2019) Deep learning for improved agricultural risk management. Bui TX, ed. Proc. 52nd Hawaii Internat. Conf. on System Sci. (HICSS, Honolulu, HI), 1033–1042. Google Scholar
- (2019) Climate change: The ultimate challenge for economics. Amer. Econom. Rev. 109(6):1991–2014.Crossref, Google Scholar
- (2016) “Why should I trust you?” Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining, 1135–1144.Google Scholar
- (2020) Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nature Food 1(2):127–133.Crossref, Google Scholar
- (2018) Federal crop insurance: Program overview for the 115th congress. Technical report, Congressional Research Service.Google Scholar
- (2015) Modeling covariance risk in Merton’s ICAPM. Rev. Financial Stud. 28(5):1428–1461.Crossref, Google Scholar
- (2021) Who benefits from robo-advising? Evidence from machine learning. Working paper, Georgetown University, Washington, DC.Google Scholar
- (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. National Acad. Sci. USA 106(37):15594–15598.Crossref, Google Scholar
- (2021) Deep learning for mortgage risk. J. Financial Econom. 19:313–368.Crossref, Google Scholar
- (2017) Achieving rational farm subsidy rates. Technical Report 113, R Street Policy Study, R Street Institute, Washington, DC.Google Scholar
- (2002) The complexity effects on choice with uncertainty: Experimental evidence. Econom. J. (London) 112(482):936–965.Crossref, Google Scholar
- (2008) Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem. Agricultural Forest Meteorology 148(3):417–427.Crossref, Google Scholar
- (2001) Weather derivatives for specific event risks in agriculture. Appl. Econom. Perspective Policy 23(2):333–351.Google Scholar
- USDA (2014) World agricultural supply and demand estimates report (WASDE). Office of the chief economist (OCE). U.S. Department of Agriculture, Washington, DC.Google Scholar
- USDA (2019) State/county/crop summary of business. Accessed August 18, 2023, https://rma.usda.gov/en/Information-Tools/Summary-of-Business/State-County-Crop-Summary-of-Business.Google Scholar
- (2019) A 2-week weather forecast may be as good as it gets. Science 363(6429):801–801.Crossref, Google Scholar
- (2017) Deep Gaussian process for crop yield prediction based on remote sensing data. Proc. 31st AAAI Conf. on Artificial Intelligence, 4559–4565.Google Scholar

