Analysis of Bootstrap Techniques for Loss Reserving

Abstract

Insurance companies must have an appropriate method of estimating future reserve amounts. These values will directly influence the rates that are charged to the customer. This thesis analyzes stochastic reserving techniques that use bootstrap methods in order to obtain variability estimates of predicted reserves. Bootstrapping techniques are of interest because they usually do not require advanced statistical software to implement. Some bootstrap techniques have incorporated generalized linear models in order to produce results. To analyze how well these methods are performing, data with known future losses was obtained from the National Association of Insurance Commissioners. Analysis of this data shows that most bootstrapping methods produce results that are comparable to one another and to the trusted Chain Ladder method. The methods are then applied to loss data from a small Midwestern insurance company to predict variation of their future reserve amounts.