A1627
Title: Conditional spatiotemporal copula model for crop insurance
Authors: Melina Mailhot - Concordia (Canada) [presenting]
Marie Michaelides - Concordia University (Canada)
Abstract: Climate change has emerged as one of the most pressing challenges of the time, impacting global ecosystems, economies and social resilience. The insurance industry stands at the forefront of the significantly affected sectors. Accurate prediction of crop yields under varying climatic conditions is paramount for designing sustainable insurance products, determining appropriate premium rates, and ensuring timely payouts that mitigate farmers' financial losses. A spatiotemporal conditional copulas are used, and both ARIMAX-GARCH models and Bayesian regime switching time series are explored for the marginal distributions, which offers a robust approach to risk assessment and premium pricing in agricultural insurance, in addition to providing reliable return level maps. Special cases with closed-form solutions, as well as a comparison between different dependence structures, are presented. An illustration using data from Ontario (Canada) is presented.
