Stochastic Asset-Liability Management Modelling for Nigerian Pension Funds Under Macroeconomic Uncertainty

This study develops a stochastic asset-liability management model for Nigerian pension funds and examines funding risk under Nigeria's distinctive macroeconomic uncertainty profile. Traditional ALM models for pension funds apply deterministic investment return and liability discount rate assumptions that fail to capture the joint distribution of asset returns and liability present values under scenarios of economic stress. In Nigeria, the correlation between inflation, interest rates, and equity returns creates specific ALM dynamics that require locally calibrated stochastic models. This study constructs a vector autoregression-based macroeconomic scenario generator calibrated to Nigerian macroeconomic time series from 2005 to 2023, incorporating inflation, short-term interest rates, equity returns, and GDP growth. Stochastic ALM projections are run for a representative Nigerian defined contribution pension fund over a 20-year horizon. Funded status distributions and contribution adequacy metrics are computed under 1000 simulated economic scenarios. Findings reveal that the distribution of fund outcomes is substantially wider than deterministic projections suggest, with a 10 percent probability of funded status falling below 60 percent of projected target by year 15 under current investment allocation assumptions. Increasing equity allocation reduces mean shortfall risk but widens tail outcomes. The study concludes that stochastic ALM modelling reveals significant hidden funding risk in Nigerian pension funds. It recommends that PENCOM require stochastic scenario testing in PFA investment policy statements and annual liability adequacy reports.

Keywords: stochastic ALM, pension fund, macroeconomic scenarios, funded status, Nigeria.