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Inconsistent Investment and Consumption Problems

机译:不一致的投资和消费问题

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摘要

In a traditional Black-Scholes market we develop a verification theorem for a general class of investment and consumption problems where the standard dynamic programming principle does not hold. The theorem is an extension of the standard Hamilton-Jacobi-Bellman equation in the form of a system of non-linear differential equations. We derive the optimal investment and consumption strategy for a mean-variance investor without pre-commitment endowed with labor income. In the case of constant risk aversion it turns out that the optimal amount of money to invest in stocks is independent of wealth. The optimal consumption strategy is given as a deterministic bang-bang strategy. In order to have a more realistic model we allow the risk aversion to be time and state dependent. Of special interest is the case were the risk aversion is inversely proportional to present wealth plus the financial value of future labor income net of consumption. Using the verification theorem we give a detailed analysis of this problem. It turns out that the optimal amount of money to invest in stocks is given by a linear function of wealth plus the financial value of future labor income net of consumption. The optimal consumption strategy is again given as a deterministic bang-bang strategy. We also calculate, for a general time and state dependent risk aversion function, the optimal investment and consumption strategy for a mean-standard deviation investor without pre-commitment. In that case, it turns out that it is optimal to take no risk at all.
机译:在传统的Black-Scholes市场中,我们针对通用的投资和消费问题类别开发了一个验证定理,而标准的动态编程原理不成立。该定理是以非线性微分方程组形式对标准Hamilton-Jacobi-Bellman方程进行的扩展。我们推导了均方差型投资者的最佳投资和消费策略,而无需预先承诺劳动力收入。在持续规避风险的情况下,事实证明,投资股票的最佳资金量与财富无关。最佳的消费策略是确定性的爆炸策略。为了有一个更现实的模型,我们允许风险规避与时间和状态有关。特别令人感兴趣的是,风险规避与现在的财富加上未来的劳动收入的金融价值减去消费成反比。使用验证定理,我们对这个问题进行了详细的分析。事实证明,投资股票的最佳金额是由财富与未来劳动收入的金融价值减去消费的线性函数给出的。最佳的消费策略再次作为确定性的爆炸策略给出。对于一般的时间和状态相关的风险规避函数,我们还计算了没有预先承诺的均值标准差投资者的最佳投资和消费策略。在那种情况下,事实证明最好是完全不冒险。

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