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A multistage stochastic programming asset-liability management model: an application to the Brazilian pension fund industry

机译：多阶段随机规划资产负债管理模型：在巴西养老基金行业中的应用

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This paper proposes a multistage stochastic programming approach for the asset-liability management of Brazilian pension funds. We generate asset price scenarios with stochastic differential equations-Geometric Brownian Motion model for stocks and Cox-Ingersoll-Ross model for fixed income securities. Intertemporal solvency regulatory rules for Brazilian pension funds are considered endogenously in the model and enforced with a combinatorial constraint. A VaR probabilistic constraint is incorporated to obtain a positive funding ratio at each time period with high probability. Our approach uses multiple trees to provide a representative characterization of the uncertainty and is not computationally prohibitive. We evaluate the insolvency probability under different initial funding ratios through extensive simulations. The study reveals that the likely decrease of interest rate premiums in the next years will force pension fund managers to significantly change their portfolio strategies. They will have to take more risk in order to deliver the cash flows required to cover the liabilities and satisfy the regulatory constraints.
机译：本文提出了一种用于巴西养老基金资产负债管理的多阶段随机规划方法。我们使用随机微分方程-股票的几何布朗运动模型和固定收益证券的Cox-Ingersoll-Ross模型生成资产价格情景。该模型内生地考虑了巴西养老基金的跨期偿付能力监管规则，并在组合约束条件下强制执行。 VaR概率约束被并入，以高概率在每个时间段获得正的资金比率。我们的方法使用多个树来提供不确定性的代表性特征，并且在计算上不是禁止的。通过广泛的模拟，我们评估了不同初始资金比率下的破产概率。该研究表明，未来几年利率溢价可能会下降，这将迫使养老基金经理大幅改变其投资组合策略。他们将不得不承担更大的风险，以提供偿还债务所需的现金流量并满足监管要求。

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