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Identifying and compensating for model mis-specification in factor risk models

机译:识别和补偿因素风险模型中的模型错误指定

摘要

Techniques for more accurately estimating the risk, or active risk, of an investment portfolio when using factor risk models are disclosed. This improved accuracy is achieved by identifying and compensating for the inherent “modeling error” present when risk is represented using a factor risk model. The approach adds one or more factors that depend on the investment portfolio and that explicitly compensate for factors that are unspecified or unattributed in the original factor risk model. These unspecified factors of the original factor risk model lead to modeling error in the original factor risk model. The approach can be used with a variety of different factor risk models, such as, fundamental, statistical and macro risk models, for example, and for a variety of securities, such as equities, international equities, composites, exchange traded funds (ETFs), or the like, currencies, and fixed-income, for example. The risk associated with modeling error in a factor risk model relative to a particular portfolio is identified and quantified. Knowledge of this risk associated with modeling error can be utilized when estimating risk, or active risk, using factor risk models or when constructing optimal portfolios by mean-variance optimization or other portfolio construction strategies and procedures that make use of factor risk models.
机译:公开了用于在使用因子风险模型时更准确地估计投资组合的风险或主动风险的技术。通过识别和补偿使用因子风险模型表示风险时存在的固有“建模误差”,可以提高准确性。该方法添加了一个或多个取决于投资组合的因素,并明确补偿了原始因素风险模型中未指定或未归因的因素。原始因子风险模型的这些未指定因子导致原始因子风险模型中的建模错误。该方法可用于各种不同的因素风险模型,例如基本,统计和宏观风险模型,并且可用于各种证券,例如股票,国际股票,合成货币,交易所交易基金(ETF)等等,例如货币和固定收益。识别和量化与因子风险模型相对于特定投资组合的建模误差相关的风险。当使用因子风险模型估算风险或活动风险时,或者通过均值方差优化或使用因子风险模型的其他投资组合构建策略和程序构建最佳投资组合时,可以利用与建模错误相关的风险知识。

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