Resilient Machine Learning Approaches for Fast Risk Evaluation and Management in Financial Portfolios and Variable Annuities

Abstract

Risk management of financial derivatives and actuarial products is intricate and often requires modeling the underlying stochasticity with Monte Carlo simulations. Monte Carlo simulation is flexible and can easily adapt to changes in model assumptions and market conditions. However, as multiple sources of risk are considered over long time horizons, the simulation model becomes complex and time-consuming to run. Tremendous research effort has been dedicated to designing computationally efficient machine learning-based procedures that mitigate the computational burden of a standard simulation procedure. In machine learning, model flexibility comes at the expense of model resilience, which is crucial for risk management tasks. This study considers estimating tail risks of complex financial and actuarial products with resilient machine learning-based nested simulation procedures. We propose a novel metamodeling approach that integrates deep neural networks within a nested simulation framework for efficient risk estimation. Our approaches offer substantial improvements over the associated standard simulation procedures. This study also illustrates how to build and assess resilient machine learning models for different problem complexities and different data structures, qualities, and quantities. To further enhance adaptability to new variable annuity contracts and changing market conditions, this thesis explores transfer learning techniques. By reusing and fine-tuning pre-trained metamodels, the proposed approach accelerates the adaptation process to different contract features and evolving market dynamics without retraining models from scratch. Transfer learning improves computational efficiency and enhances the robustness and flexibility of neural network metamodels in dynamic hedging of variable annuities.

Extensive numerical experiments in this thesis demonstrate that the proposed methods substantially improve computational efficiency, sometimes shortening runtime by orders of magnitude compared to standard nested simulation procedures. The results indicate that deep neural network metamodels with transfer learning can quickly adapt to new market scenarios and contract specifications. This research contributes to the advancement of risk management practices for complex actuarial products and financial derivatives. By leveraging advanced machine learning techniques, this thesis offers a practical and scalable solution for insurers to perform timely and accurate risk assessments. The integration of long short-term memory metamodels and transfer learning into a nested simulation framework represents a major step forward toward more efficient, adaptable, and robust methodologies in actuarial science and quantitative finance.