Asset-liability management (ALM) is a challenging task faced by pension funds due to the
uncertain nature of future asset returns and interest rates. To address this challenge, this paper
presents a new mathematical model that uses aWorst-case Conditional Value-at-Risk (WCVaR)
constraint to ensure that the funding ratio remains above a regulator-mandated threshold with a
high probability under the worst-case probability distribution that plausibly explains historical
sample data. A tractable reformulation of this WCVaR constraint is developed based on the
definition and a new reformulation/approximation of the Worst-case Lower Partial Moment
(WLPM) for a general loss function. Additionally, a new data-driven moment-based ambiguity
set is developed to capture uncertainty in the moments of random variables in the ALM problem.
The proposed approach is evaluated using real-world data from the Canada Pension Plan (CPP)
and is shown to outperform classical ALM models, based on either CVaR orWCVaR with fixed
moments, on out-of-sample data. The proposed framework for handling correlated uncertainty
using WCVaR with nonlinear loss functions can be used in other application areas.