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

摘要

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.