A practical prioritization scheme for goal-based asset–liability management

We present an individual asset-liability management problem characterized, over a potentially long-term horizon, by financial-, consumption- and retirement-type of goals whose achievement probability is maximized under a rich set of constraints in a stochastic linear programming formulation. The decision maker will determine an optimal multistage asset-liability strategy based on a mean-risk paradigm in which the risk is captured by the terminal Conditional Value-at-Risk (CVaR) measure, and the mean by the expected wealth plus a function of the goals. We introduce a simple scheme based on subjective priority levels to enforce and then estimate after solution the probability to attain the targets. The scheme is applied to an optimization problem with stochastic goals, random asset returns and liability costs, random income and consumption costs, as well as random retirement income upon retirement. Computational evidence is presented for the case of 2 or 5 goals and we show that solution times of a set of large scale linear programs do not depend on the number of goals, nor do goal achievement probabilities depend on the adopted mean-CVaR trade-off.