基于归纳因果搜索算法的投资组合风险分解

Brian D. Deaton
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引用次数: 0

摘要

提出了一种沿独立因素估计和分解投资组合风险的方法。这种分解基于市场潜在的独立风险因素,这些风险因素是通过使用基于独立成分分析的归纳因果搜索算法通过经验发现的。由于可以理解独立的风险因素总是会给投资组合增加风险,因此投资组合经理可以使用它们来更好地理解和预算风险。相比之下,使用经典边际分析的投资组合管理是令人困惑的,因为在投资组合中添加有风险的证券实际上可能会降低投资组合的风险。在使用六种最广泛交易的货币(澳元、加元、欧元、英镑、日元和美元)的小型应用程序中,在投资组合优化期间约束了独立因素风险贡献,并且发现最终投资组合的内部风险特征与使用对原始资产风险贡献的约束所产生的内部风险特征相比较有利。
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Decomposition of Portfolio Risk into Independent Factors Using an Inductive Causal Search Algorithm
A method is presented to estimate and decompose a portfolio's risk along independent factors. This decomposition is based upon a market's underlying independent risk factors, which are found empirically by using an inductive causal search algorithm that is based on independent component analysis. Since independent risk factors can be understood to always add risk to a portfolio, a portfolio manager can use them to better understand and budget risk. In contrast, portfolio management using the classic marginal analysis is confusing because adding a risky security to a portfolio might actually reduce the portfolio's risk. In a small application using the six most widely traded currencies (the Australian dollar, Canadian dollar, euro, sterling, Japanese yen and US dollar), independent-factor risk contributions are constrained during portfolio optimizations, and the internal risk characteristics of the resulting portfolios are found to compare favorably with those created by using constraints on the risk contributions of the original assets.
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