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Optimal selection portfolio problem: a semi-linear PDE approach

机译:最优选择投资组合问题:半线性PDE方法

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摘要

In this paper, we consider the problem of optimal portfolio choice for an investor who wants to maximize the utility of his/her terminal wealth. This work is an overview of the PDE approach for the optimization problem resolution. This approach consists in studying the Hamilton-Jacobi-Bellman equation (HJB equation) associated with the investment problem. In the first part, we consider an investment problem with stochastic volatilities and portfolio constraints. The value function of the investment problem is a viscosity solution of the fully nonlinear HJB equation which can be solved when the risky asset number is low. When the risky asset number is high, the numerical study of the HJB equations is costly. The second section deals with the investment problem with constraints on proportion of the wealth invested in risky assets. This part illustrates the results of Bouzguenda et al. (2009) who studied the backward stochastic differential equations associated with the transformed semi-linear equation and suggested a numerical scheme for the resolution based on the iterative regressions on functions bases and Monte Carlo Method.
机译:在本文中,我们考虑了想要最大化其终端财富效用的投资者的最优投资组合选择问题。这项工作是用于优化问题解决的PDE方法的概述。这种方法在于研究与投资问题相关的汉密尔顿-雅各比-贝尔曼方程(HJB方程)。在第一部分中,我们考虑具有随机波动率和投资组合约束的投资问题。投资问题的价值函数是完全非线性HJB方程的粘性解,可以在风险资产数量较低时解决。当风险资产数量很高时,HJB方程的数值研究成本很高。第二部分处理投资问题,该投资问题限制了投资于风险资产的财富比例。这部分说明了Bouzguenda等人的结果。 (2009年)研究了与转换后的半线性方程式相关的后向随机微分方程,并提出了基于函数基和蒙特卡洛方法的迭代回归的分辨率数值方案。

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