Estimating extreme cancellation rates in life insurance

Francesca Biagini,Johannes G Jaspersen,Andrea Mazzon,Tobias Huber

doi:10.1111/jori.12336

Francesca Biagini, Johannes G Jaspersen + Show 2 more

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https://doi.org/10.1111/jori.12336

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Abstract

AbstractThis paper assesses the risk of a mass lapse event in life insurance. The rarity of the event and the complexity of policyholder behavior make the risk assessment of such a scenario difficult. Using a simulation study, we evaluate how different estimation methods can assess the risk of this scenario, using panel data at the company level. We then use the best‐performing method to estimate the probability distribution function of a mass cancellation event in the United States and Germany. We identify dependencies of the event on company and country characteristics, which have not been taken into account by regulating agencies. We also find that the current mass lapse scenario in Solvency II has no empirical foundation for the German market. We show that an empirically valid scenario leads to a significantly lower solvency capital requirement for the average German life insurer.

Highlights

Rare events with extreme consequences often have a profound influence on economies and their economic agents
We use the dynamic peaks over the threshold method developed by Chavez‐Demoulin et al (2016) to take quantitative covariates into account. We apply this approach to U.S data and show that, depending on product type, cancellation rates up to 50% are a good assumption for a mass cancellation scenario in this market
Cancellation rates in the range of 20%–25% reflect the risk of a mass cancellation scenario in the German market

Summary

Introduction

Rare events with extreme consequences often have a profound influence on economies and their economic agents. The 9/11 terrorist attacks led to considerable changes in many economic sectors, such as the airline industry Hurricanes, such as Katrina and Sandy, have shaped how we see flood protection and insurance. Even though policyholder‐cancellation behavior has received considerable attention in the academic literature (e.g., Eling & Kiesenbauer, 2013; Kuo et al, 2003), the question of how to model mass cancellation scenarios has received scant attention. This is surprising because the possibility of mass cancellations has a large effect on insurance companies' asset liability management and leads to one of the largest financial reserves in the European risk management framework Solvency II (EIOPA, 2011)

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