A STOCHASTIC FRAMEWORK FOR OPTIMIZING HYBRID PENSION SCHEMES IN NIGERIA: ASSET ALLOCATION AND CONTRIBUTIONS UNDER ECONOMIC REGIMES

Abstract

This study examines the design, evaluation, and optimization of asset allocation and contribution rates for hybrid pension schemes in Nigeria by integrating Regime-Switching Geometric Brownian Motion (RS-GBM) with Markov Chain models to capture macroeconomic regime dynamics and their impact on pension fund performance. Behavioral survey variables, including pension awareness, risk tolerance, and contribution behavior, are incorporated into the stochastic model to reflect real-world contributor heterogeneity. Monte Carlo simulations of 10,000 pension fund trajectories per scenario were conducted over a ten-year period, analyzing the effects of economic regime shifts, contribution patterns, and behavioral factors on fund sustainability and retirement Introduction Pension systems worldwide are increasingly under pressure due to demographic prolonged life shifts, expectancy, inflationary financial pressures, market and instability (OECD, 2023; World Bank, 2022). Across both developed and developing economies, rising old-age dependency ratios have intensified regarding sustainability systems the of (OECD, concerns long-term retirement 2023). Population aging continues to increase pension income adequacy. Results demonstrate that hybrid pension schemes reduce downside risk relative to pure Defined Benefit (DB) or Defined Contribution (DC) plans and that higher contributor awareness and risk tolerance enhance expected fund accumulation. The study also highlights the role of annuitization in providing stable retirement income under volatile economic conditions. These findings offer quantitative insights for policymakers and pension administrators, supporting evidence-based design of sustainable and effective hybrid pension schemes in Nigeria.