英文标题：
《Learning and Portfolio Decisions for HARA Investors》
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作者：
Michele Longo and Alessandra Mainini
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最新提交年份：
2015
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英文摘要：
We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market price of risk constant in sign, the ratio between the portfolio under partial observation and its myopic counterpart increases with respect to risk tolerance. As a consequence, the absolute value of the partial observation case is larger (smaller) than the myopic one if the investor is more (less) risk tolerant than the logarithmic investor. Moreover, our explicit computations enable to study in details the so called hedging demand induced by learning about market price of risk.
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中文摘要：
当风险的市场价格是一个不可观测的随机变量时，我们使HARA投资者的终端财富的预期效用最大化。我们显式地计算最优投资组合，并通过将其与相应的近视策略进行比较来探索学习的效果。特别是，我们表明，对于符号为风险常数的市场价格，部分观察下的投资组合与其近视对应投资组合之间的比率随着风险承受能力的增加而增加。因此，如果投资者比对数投资者的风险容忍度更高（更低），则部分观察案例的绝对值比近视案例的绝对值大（更小）。此外，我们的显式计算能够详细研究通过了解风险的市场价格而产生的所谓对冲需求。
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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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