涓婚锛欰gricultural Reinsurance Ratemaking: Development of a New Premium Principle
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Wenjun Zhu is currently an Assistant Professor in the School of Finance at the Nankai University, China. She achieved her Ph.D. in the Department of Statistics and Actuarial Science, University of Waterloo in August 2015. As the first student admitted to the PhD program without a Master's degree in the department, Dr. Zhu holds double Bachelor's degree in Economics as well as Informatics and Computational Mathematics.
She is also twice a winner of the Society of Actuaries James C. Hickman Scholar (2013-14, 2014-15). Her PhD thesis focuses on developing effective actuarial and statistical tools for agricultural risk management. In particular, she has interdisciplinary research experience in agribusiness, finance and insurance modeling, systemic weather risk management, statistical inference with copulas, and actuarial credibility models. Dr. Zhu has publications in journals such as the Journal of Banking & Finance. She also has several papers under review of Journal of Risk and Insurance, American Journal of Agricultural Economics, etc.
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Developing a scientific methodology for agricultural insurance products is challenging due to a number of unique features compared to other commercial lines of Property & Casualty insurance, including moral hazard, adverse selection, shortness of data, and other structural factors of the loss experience. This is the first paper to formally introduce premium principles to the agricultural insurance and reinsurance sectors. In particular, this paper proposes a new premium principle based on the multivariate weighted distribution in order to scientifically reweight historical loss experience using auxiliary variables to refine the pricing framework. The auxiliary variables could include material information for pricing, such as economic and market conditions, liability, weather, soil, and other important information. In the empirical analysis, a number of potential premium principles commonly used in actuarial science were tested and compared, using a unique data set comprised of reinsurance experience in Manitoba from 2001 to 2011. The results show that integrating auxiliary variables into the pricing framework improves the accuracy of the rating by redistributing premium rates and assigning higher loadings to riskier reinsurance contract layers, helping reinsurers achieve better sustainability in the long term.