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Solving ALM problems via sequential stochastic programming

机译：通过顺序随机编程解决ALM问题

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In this paper, an approximation of dynamic programming using sequential stochastic programming is introduced to solve long-term dynamic financial planning problems. We prove that by approximating the true asset return dynamics by a set of scenarios and re-solving the problem at every time-step, we can solve in principle the dynamic programming problem with an arbitrarily small error. The dynamic programming algorithm is effected on the approximate sample return dynamics by means of stochastic programming. This method is applied to the problem of a fund that guarantees a minimal return on investments. This minimal return guarantee is the liability of the fund. The dynamic portfolio management problem consists of maximizing the multi-period return while limiting the shortfall with regard to the guaranteed return. The problem is tested in an 8 year out-of-sample backtest from the perspective of a Swiss fund that invests domestically and in the EU markets and faces transaction costs.
机译：本文介绍了一种采用顺序随机规划的动态规划近似方法，以解决长期动态财务规划问题。我们证明，通过用一组场景近似真实的资产收益动态并在每个时间步长重新解决问题，我们可以从原则上解决带有任意小的误差的动态规划问题。动态规划算法通过随机规划影响近似的样品返回动力学。此方法适用于保证最低投资回报的基金问题。这种最小的回报保证是基金的责任。动态投资组合管理问题包括最大化多期收益，同时限制与保证收益有关的缺口。从一家在国内和欧盟市场进行投资并面临交易成本的瑞士基金的角度，对该问题进行了为期8年的样本外回测。

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