On the Estimation of Own Funds for Life Insurers: A Study of Direct, Indirect, and Control Variate Methods in a Risk-Neutral Pricing Framework

Battle Count: 94

The paper is primarily focused on insurance solvency capital calculation and actuarial valuation methods. While the Monte Carlo variance reduction techniques (control variates) are broadly applicable to quantitative finance, the specific context of life insurance ALM, Solvency II regulation, and policyholder cash flow modeling has limited direct relevance to quantitative trading strategies. The risk-neutral pricing framework and nested simulation concepts are transferable but the application domain is distinct.

The control variate framework is modular and can serve as a drop-in replacement for standard direct estimators in existing Monte Carlo frameworks. However, implementing the full mixed estimator family requires computing 2^T potential estimators, estimating optimal coefficients via regression, and managing the nested Monte Carlo simulation structure. The mathematical framework is elegant but practical implementation requires careful handling of conditional expectations, risk-neutral measures, and computational overhead for coefficient estimation. The paper notes that computational overhead is limited since all quantities are already computed during simulation.

Source code is publicly available on Fraunhofer Gitlab. The openIRM model is publicly accessible. Base parameter settings are fully documented in Table 1. However, the paper does not provide a GitHub repository link directly, and some implementation details of the nested Monte Carlo framework may require additional context from referenced papers.