Forecasting pension fund liabilities through multivariate time series models with structural breaks and demographic statistical trend analysis
Department of Mathematics, Kwame Nkrumah University of Science and Technology, Ghana.
Research Article
World Journal of Advanced Research and Reviews, 2020, 05(03), 219-238
Publication history:
Received on 03 March 2020; revised on 23 March 2020; accepted on 29 March 2020
Abstract:
Pension fund sustainability remains a critical challenge for policymakers and financial managers, particularly in aging economies where liabilities increasingly outpace contributions. Accurately forecasting pension fund liabilities is essential for ensuring long-term solvency and supporting strategic asset allocation, policy reform, and demographic planning. This study presents a robust framework that integrates multivariate time series models with structural break detection and demographic statistical trend analysis to improve the precision and reliability of pension liability forecasts. At the broader level, we recognize that pension liabilities are influenced by dynamic interdependencies among macroeconomic variables (e.g., wage growth, inflation, interest rates), institutional policy shifts, and demographic patterns such as life expectancy and retirement age. We apply vector autoregression (VAR) and vector error correction models (VECM) augmented with structural break identification to detect regime changes caused by economic shocks, legislative reforms, or shifts in labor market dynamics. This temporal sensitivity enhances model responsiveness to real-world discontinuities, thereby improving liability projections. Additionally, we incorporate cohort-based demographic forecasting methods, including Lee-Carter and Bayesian age-period-cohort (APC) models, to account for longevity risk and population heterogeneity. Our results, validated on pension fund data from three OECD countries over a 30-year span, demonstrate that integrating structural breaks and demographic statistical trends significantly reduces forecast errors compared to traditional actuarial approaches. The proposed hybrid methodology not only improves liability projection accuracy but also provides insights into long-term funding gaps, enabling pension fund administrators and public finance stakeholders to design preemptive strategies. This framework offers a scalable decision-support tool for cross-national pension systems grappling with demographic shifts and fiscal pressures.
Keywords:
Pension Liabilities Forecasting; Structural Breaks; Demographic Trend Analysis; Multivariate Time Series; Longevity Risk; Pension Sustainability
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0