Dynamic Persistence and Nonlinear Resilience: A Hybrid Econometric–Machine Learning Framework for Life Insurance Market Forecasting

Evidence from Five OECD Countries (2005–2022)

Abstract

This paper develops and validates a hybrid forecasting framework for life insurance market dynamics that integrates panel econometrics with machine learning. Using a panel dataset of OECD countries spanning 2005-2022, we construct dynamic features including lag structures, volatility proxies, trend components, and a stability index. Our empirical strategy employs fixed-effects panel models, gradient boosting (XGBoost), and ensemble aggregation. The results reveal three fundamental insights: (1) life insurance markets exhibit near-perfect autoregressive persistence, with the first lag (y_{i,t-1}) explaining approximately 75\% of predictive variance; (2) risk-related variables (volatility, stability) show weak linear effects but contribute meaningfully through nonlinear machine learning interactions; (3) the 2008–2009 financial crisis has negligible direct predictive importance, as its effects are fully mediated through the dynamic lag structure. The ensemble model achieves outstanding out-of-sample performance  (RMSE = 4.23, MAE = 2.81, MAPE = 8.30%, R² = 0.971), significantly outperforming benchmark linear models. We conclude that life insurance markets are fundamentally autoregressive systems where the primary forecasting challenge is not identifying external shocks but accurately modeling temporal persistence. Our framework offers both theoretical clarity and practical utility for actuaries, regulators, and industry forecasters. We note that these results are specific to the five countries studied (Poland, Switzerland, Turkey, Australia, Denmark) and require validation on broader samples.