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Insurance risk transfer and categorization of reinsurance contracts
 
Author:Gurenko, Eugene N.; Itigin, Alexander; Wiechert, Renate; Collection Title:Policy Research working paper ; no. WPS 6299
Country:World; Date Stored:2012/12/27
Document Date:2012/12/01Document Type:Policy Research Working Paper
SubTopics:Debt Markets; Hazard Risk Management; Labor Policies; Insurance & Risk Mitigation; Insurance LawLanguage:English
Region:The World RegionReport Number:WPS6299
Volume No:1 of 1  

Summary: Despite the existence of numerous quantitative approaches to the categorization of financial reinsurance contracts, often insurance regulators may find the practical implementation of the task to be technically challenging. This research paper develops a simple, affordable, and robust regulatory method that can help insurance regulators to categorize financial reinsurance contracts as reinsurance or financial instruments. By reviewing real examples of different categorization methods, this paper explains how the proposed method standardizes such categorization. It also summarizes the existing pertinent literature on the subject with the view to helping insurance regulators to first apply some simple indicators to flag the main issues with financial reinsurance contracts that may need further reviews. Having identified the suspicious reinsurance contracts, supervisors may consider several solutions provided by the authors and, in some cases, requiring further quantitative testing of risk transfer contracts for categorization purposes, supervisors may also consider adopting the Standardized Expected Reinsurer's Deficit approach to contract testing presented in this paper. The approach advocates the use of a simple standardized stochastic method that would allow market participants and regulators to perform robust quantitative tests quickly and at an affordable cost. Besides addressing the obvious drawbacks of the "10-10" test, the proposed alternative method allows a great reduction in the technical challenges posed to the users of the Expected Reinsurer's Deficit approach based on full stochastic models with only a minimum loss of predictive accuracy.

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