A closed-form expansion for the conditional expectations of the extended CIR process. 2022

Sanae Rujivan, and Nopporn Thamrongrat
Center of Excellence in Data Science for Health Study, Division of Mathematics and Statistics, School of Science, Walailak University, Nakhon Si Thammarat 80161, Thailand.

This paper derives a closed-form expansion for the conditional expectation of a continuous-time stochastic process, given by for , where evolves according to the extended Cox-Ingersoll-Ross process, for any functions f and g. We apply the Feynman-Kac theorem to state a Cauchy problem associated with and solve the problem by using the reduction method. Furthermore, we extend our method to any piecewise function f; demonstrating our method can be applied to price options in financial derivative markets. In numerical study, we employ Monte Carlo simulations to demonstrate the performance of the current method.

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