Estimating the Parameter Risk of a Loss Ratio Distribution—Revisited

By Avraham Adler

Download PDF of Full Text


When building statistical models to help estimate future results, actuaries need to be aware that not only is there uncertainty inherent in random events (process risk), there is also uncertainty inherent in using a finite sample to parameterize the models (parameter risk). This paper revisits Van Kampen (2003) in replicating its bootstrap method and compares it with measures of parameter uncertainty developed using maximum likelihood estimation and Bayesian MCMC analysis.

Keywords: Parameter risk, bootstrap, maximum likelihood, Bayesian MCMC, JAGS, Stan, R, approximate Bayesian computation


Adler, Avraham, "Estimating the Parameter Risk of a Loss Ratio Distribution—Revisited," Variance 9:1, 2015, pp. 114-139.

Taxonomy Classifications

Subscribe to the RSS Feed

Email List

Sign up today for the Variance e-mail list and receive updates about new issues, articles, and special features.

Mission Statement

Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.