#### Document Type

Dissertation

#### Keywords

Bootstrapping, Asymptotic Theory, Casualty and Loss, Reserves, Ratemaking, Risk Capital, Risk Modeling

#### Publisher

Bryant University

#### Abstract

One of the key functions of a property and casualty (P&C) insurance company is loss reserving, which calculates how much money the company should retain in order to pay out future claims. Most P&C insurance companies use non-stochastic (non-random) methods to estimate these future liabilities. However, future loss data can also be projected using generalized linear models (GLMs) and stochastic simulation. Two simulation methods that will be the focus of this project are: bootstrapping methodology, which resamples the original loss data (creating pseudo-data in the process) and fits the GLM parameters based on the new data to estimate the sampling distribution of the reserve estimates; and asymptotic theory, which resamples only the GLM parameters (fitted from an original set of data) from a multivariate normal distribution to estimate the sampling distribution of the reserve estimates. Using Excel, R, and SAS software, the copulas of the GLM parameter estimates from the stochastic methods will be compared to the copula from a multivariate normal distribution. Ultimately, the Value at Risk (VaR) and Tail Value at Risk (TVaR) results from each method’s sampling distribution will be compared to each other, with the goal of showing that the two methods produce significantly different reserve estimates and risk capital estimates at the low end of the reserve distribution. This would answer the question as to whether the asymptotic theory procedure sufficiently approximates real-world scenarios.

#### Recommended Citation

DiFronzo, Andrew J. Jr., "Bootstrapping vs. Asymptotic Theory in Property and Casualty Loss Reserving" (2015).*Honors Projects in Mathematics.*Paper 18.

https://digitalcommons.bryant.edu/honors_mathematics/18

#### Included in

Applied Mathematics Commons, Multivariate Analysis Commons, Probability Commons, Statistical Models Commons, Statistical Theory Commons