Zhiyi Shen:A Regression-based Monte Carlo Approach for Pricing Polaris Variable Annuities




报告人:Zhiyi Shen(PhD Candidate, University of Waterloo)

报告题目:A Regression-based Monte Carlo Approach for Pricing Polaris Variable Annuities

Abstract:In this talk, I will present some results of my research papers[1,2] jointly with Professor Chengguo Weng. We develop and generalize the Least Squares Monte Carlo (LSMC) algorithm to solve a wide class of stochastic optimal control problems. In contrast to most variants of LSMCs, our algorithm simulates the post-action values of the state process to circumvent the thorny issue of forward simulation in absence of optimal control policies. We further introduce a shape-preserving sieve estimation technique to approximate the continuation function involved in the Bellman equation. Our algorithm poses a number of advantages such as memory reduction, preserving the convexity and monotonicity of the value function, and obviating computationally expensive tuning parameter selection.  As an application, we employ the algorithm to evaluate the no-arbitrage price of the “Polaris Choice IV” variable annuities recently issued by the American International Group.


Zhiyi Shen, and Chengguo Weng. Pricing bounds and bang-bang analysis of the Polaris variable annuities. Available at SSRN: https://ssrn.com/abstract=3056794, 2018.

Zhiyi Shen, and Chengguo Weng. A shape-preserving sieve estimation approach for stochastic optimal control model. Working paper, 2018.