Small Sample Stochastic Tail Modeling: Tackling Sampling Errors and Sampling Bias by Pivot-Distance Sampling and Parametric Curve Fitting Techniques

Authors

Paul H. Johnson Jr., University of Illinois at Urbana-ChampaignFollow
Chinmei Y. Chueh, Central Washington UniversityFollow
Yongxue Qi, University of Illinois at Urbana-ChampaignFollow

Document Type

Article

Department or Administrative Unit

Mathematics

Publication Date

8-2012

Abstract

We describe two original open source software applications that have been developed to aid model efficiency studies: (1) CSTEP (Cluster Sampling for Tail Estimation of Probability) for reducing sampling error through variations of distance sampling and cluster/pivot processes; and (2) AMOOF2 (Actuarial Model Outcome Optimal Fit Version 2.0) for mitigating small sample bias in parametric, time-efficient probability density function fitting. CSTEP uses the scenario reduction method of representative scenarios to sample scenarios from a population of stochastic scenarios to obtain a sample-run distribution of a financial outcome that can be analyzed by AMOOF2 to fit the optimal probability density function.

Recommended Citation

Johnson, P., Chueh, Y., & Qi, Y. (2013). Small Sample Stochastic Tail Modeling: Tackling Sampling Errors and Sampling Bias by Pivot-Distance Sampling and Parametric Curve Fitting Techniques. ARCH 2013.1 Proceedings.

Journal

ARCH Proceedings

Rights

Copyright © 2013 Society of Actuaries