Optimal long-term Tier 1 employee pension management with an application to Chinese urban areas
从投资委员会视角构建随机优化模型，结合多阶段随机规划与动态随机控制，应用于中国城市养老金体系，旨在减少政府干预并提升资金可持续性。
We formulate a stochastic optimization problem from the perspective of an investment committee responsible for Tier 1 social security pension policies and whose decisions are bound to have relevant economic and social consequences. The adopted modelling approach combines canonical multistage stochastic programming (MSP) with dynamic stochastic control (DSC): the first applies to the short-medium term, the second to the long-term. Through the combined framework, we are able to span a long planning horizon without jeopardizing the accuracy of scenario tree based medium-term planning. We apply this methodology to the Chinese pension system, which relies on two large reference areas for rural and urban populations. In this article, we concentrate on the ever-growing urban public pension system, which is facing significant challenges due to a declining workforce and a rapidly ageing population. This welfare area, originally conceived as a pay-as-you-go (PAYG) system, has undergone several recent reforms to enhance its long-term sustainability and reduce the interventions of the central government required to improve its funding condition. Among those relevant in our setting, is the reduction of policy constraints that until 2015 severely limited the possibility to invest in assets other than traditional, locally traded, long-term fixed income securities. We propose an optimization model in which the decisions of the investment management aim at significantly reducing central government interventions as a last resort liquidity provider and progressively improving the system funding condition. A rich set of computational and economic evidence is presented to validate the methodology and clarify its potential benefits to pension system efficiency.