As more regulatory reporting requirements in the regulatory regimes around the world move towards dependence on stochastic approaches, insurance companies are experiencing increasing difficulty with detailed forecasting and more accurate valuation and risk assessment based on Monte Carlo simulations.

Stochastic modeling is commonly used by financial reporting actuaries whenever reporting procedures, such as reserving and capital requirement calculation, are performed under various economic scenarios, which are stochastically determined. Nested stochastic modeling is required whenever modeling components under each economic scenario are determined by stochastic scenarios in the further future.

Many existing techniques to speed up nested simulations are based on the reduction of inner loop calculations by curve fitting techniques. The essence of these techniques is to develop a functional relationship between risk factors (equity values, interest rates, etc) and target features (insurance liability or their greeks) of inner loop calculations. Such functional relationship can be approximated by multivariate interpolation or smoothing techniques such as least squares Monte Carlo. Nonetheless, these techniques often require a large size of economic scenarios to develop accurate enough functional relationships, which could also be very costly to begin with. The new Sample Recycling technique is based on an entirely different strategy, which is to avoid “approximate” functional relationship but instead to save time by recycling a limited set of economic scenarios.

This project is intended to provide a visualization of sample recycling methods and to make the new technology accessible to practitioners. The research team is expected to make a Youtube video to illustrate the technology.

Students: Zhangyao Chen, Hao Gai, Wilson Jonathan Phurwo, Jeremy Soriano, Samuel Woessner

Supervisor: Runhuan Feng

Youtube Video: Click here