本文研究的是掌握可违约债券内幕信息的投资者的期望效用投资组合问题。内幕人士可以将资金分配到无风险资产,风险资产和可违约债券上。在以往研究中,内幕信息来源于扩散型风险资产过程,而本文假设投资者掌握的内幕信息是关于债券违约的预期信息。本文的研究表明,与仅使用公开信息相比,内幕信息能显著提高投资策略的效益。本文在以下两种内幕信息下借助扩大过滤技术结合鞅方法得到了最优投资策略:一种情况是在未来某个确定的日期发生违约,另一种情况是在投资到期前不可能违约。This paper investigates the expected utility portfolio optimization problem with inside information about defaultable bonds. The insider can allocate his funds to a risk-free asset, a risky asset and a defaultable bond. In previous studies, inside information comes from a diffuse risky asset process, whereas this paper assumes that the inside information possessed by the investor is expected information about the bond default. The research in this paper shows that inside information significantly improves the effectiveness of an investment strategy compared to using only publicly available information. The paper obtains the optimal investment strategy with the help of the expanded filtering technique combined with the martingale method under two types of inside information: a situation where default occurs at a certain date in the future and a situation where default is unlikely to occur before the maturity of the investment.
投资组合优化问题中的输入参数大多是由历史数据估计而来,估计的不确定性可能对Markowitz投资组合模型产生巨大的影响.近期,一个联合估计与鲁棒性的优化框架(joint estimation and robustness optimization,JERO)被提出,通过结合参数估计和优化问题以减弱估计不确定性对优化问题的影响.JERO框架被应用到投资组合优化领域(JERO with the mean return and the risk(variance)constraints,JERO-MV),同时考量了投资组合模型中有价值的两个度量:投资组合的回报和风险.但该模型可能会导致投资组合过分集中于某几个资产,这将增加投资风险和成本.本文在JERO-MV模型的基础上增加分散化约束,并给出该模型的可行性条件.本文在真实数据集上进行了大量的数值实验,并与JERO-MV模型进行对比.在大多数情形下,我们的模型都有更好的样本外表现.
