Determining the appropriate premium to charge for the underlying risk is central to delivering a sustainable agricultural insurance program. While this is fundamental to all types of insurance, in agriculture this is a particularly challenging task given systemic risk, information asymmetry, and...

The focus of this paper is on predictive analytics regarding data scarcity and credibility, which are major difficulties facing the agricultural insurance sector often due to limited loss experience data for those infrequent, but, extreme weather events. A new relational data-matching model is...

Lévy subordinated hierarchical Archimedean copulas (LSHAC) are flexible models in high dimensional modeling. However, there is limited literature discussing their applications, largely due to the challenges in estimating their structures and their parameters. In this paper, we propose a...

This paper proposes a new copula model known as the Lévy subordinated hierarchical Archimedean copulas (LSHAC) for multi-country mortality dependence modeling. To the best of our knowledge, this is the first paper to apply the LSHAC model to mortality studies. Through an extensive empirical...

PurposeThe purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed...

This paper proposes a new class of premium calculation principles based on the multivariate weighted distribution, where risk loadings are imposed by transforming the density of the underlying actuarial risk by encompassing a number of external risk factors. This is a highly flexible class of...