Case Studies for Model Efficiency: Special Sampling and MLE Bias Correction

نویسندگان

  • Yvonne C. Chueh
  • Paul H. Johnson
چکیده

Driven by the rising need for model efficiency to enable timely and accurate data analytics, stochastic modelers in the actuarial community have been seeking innovative alternatives to increase model reality, dynamics, capacity, speed, and precision. These challenges have motivated this paper to demonstrate free research software tools for case studies in financial stochastic modeling. Case studies considered in this paper use two free software applications: (1) CSTEP (Cluster Sampling for Tail Estimation of Probability) for reducing sampling bias error with specialized user-defined distance sampling, and (2) AMOOF2 (Actuarial Model Outcome Optimal Fit Version 2.0) for correcting small-sampling bias from fitting parametric probability density functions via maximum likelihood estimation to a small sample. These two software applications were used to reveal insights regarding the probability distribution of statutory ending surplus for a large business block of life annuity contracts, while comparing special sampling techniques that affect outcomes and model efficiency, assessed by the bias of fitting error.

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تاریخ انتشار 2014