MCFAM Seminar - Neural Networks for Pricing Rainbow Options

Speaker: ​Christopher Najjar, Moody's

​Abstract: The valuation of complex financial instruments presents an increasing challenge in actuarial reporting. In the absence of closed‑form solutions, traditional pricing approaches typically rely on nested stochastic risk‑neutral simulations at each projection period. These methods can lead to substantial computational burdens, resulting in longer runtimes and increased infrastructure costs. This research investigates the application of neural networks as an efficient and flexible alternative to brute‑force simulation techniques. By constructing a proxy valuation framework trained on stochastic model outputs, the proposed approach delivers option valuations in a fraction of the computational time while preserving a high degree of accuracy. The results demonstrate that neural networks can serve as a practical and scalable solution for pricing complex derivatives such as rainbow options in actuarial and risk‑management applications.