英文标题：
《Expected Utility Maximization and Conditional Value-at-Risk
Deviation-based Sharpe Ratio in Dynamic Stochastic Portfolio Optimization》
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作者：
Sona Kilianova, Daniel Sevcovic
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最新提交年份：
2018
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英文摘要：
In this paper we investigate the expected terminal utility maximization approach for a dynamic stochastic portfolio optimization problem. We solve it numerically by solving an evolutionary Hamilton-Jacobi-Bellman equation which is transformed by means of the Riccati transformation. We examine the dependence of the results on the shape of a chosen utility function in regard to the associated risk aversion level. We define the Conditional value-at-risk deviation ($CVaRD$) based Sharpe ratio for measuring risk-adjusted performance of a dynamic portfolio. We compute optimal strategies for a portfolio investment problem motivated by the German DAX 30 Index and we evaluate and analyze the dependence of the $CVaRD$-based Sharpe ratio on the utility function and the associated risk aversion level.
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中文摘要：
本文研究了一类动态随机投资组合优化问题的期望终端效用最大化方法。我们通过求解演化的Hamilton-Jacobi-Bellman方程来数值求解它，该方程通过Riccati变换进行变换。我们检验了结果对所选效用函数形状与相关风险厌恶水平的依赖性。我们定义了基于条件风险价值偏差（CVaRD$）的夏普比率，用于衡量动态投资组合的风险调整绩效。我们计算了一个由德国DAX 30指数驱动的组合投资问题的最优策略，并评估和分析了基于美元CVaRD的夏普比率对效用函数和相关风险厌恶水平的依赖性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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