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1. 发明申请

US20100153307A1 Identifying and Compensating for Model Mis-Specification in Factor Risk Models 有权
标题翻译：在因子风险模型中识别和补偿模型误差规范
公开(公告)号：US20100153307A1
公开(公告)日：2010-06-17
申请号：US12711554
申请日：2010-02-24
申请人： Robert A. Stubbs , Stefan H. Schmieta
发明人： Robert A. Stubbs , Stefan H. Schmieta
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
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.
摘要翻译：披露了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。当使用因子风险模型表示风险时，通过识别和补偿存在的固有“建模误差”来实现这种改进的准确性。该方法增加了一个或多个依赖于投资组合的因素，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。原始因素风险模型的这些未明确因素导致原始因素风险模型的建模误差。该方法可用于各种不同因素风险模型，如基本面，统计学和宏观风险模型，以及各种证券，如股票，国际股票，复合材料，交易所交易基金（ETF）等。，等等，货币和固定收益。与特定投资组合相关的因素风险模型中的建模误差相关风险被识别和量化。使用因子风险模型估计风险或主动风险时，可以利用与建模误差相关的风险的知识，或者通过均值方差优化或利用因子风险模型的其他投资组合构建策略和程序来构建最优投资组合。

2. 发明申请

US20070179908A1 Identifying and Compensating for Model Mis-Specification in Factor Risk Models 有权
标题翻译：在因子风险模型中识别和补偿模型误差规范
公开(公告)号：US20070179908A1
公开(公告)日：2007-08-02
申请号：US11668294
申请日：2007-01-29
申请人： Robert A. Stubbs , Stefan H. Schmieta
发明人： Robert A. Stubbs , Stefan H. Schmieta
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
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.
摘要翻译：披露了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。当使用因子风险模型表示风险时，通过识别和补偿存在的固有“建模误差”来实现这种改进的准确性。该方法增加了一个或多个依赖于投资组合的因素，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。原始因素风险模型的这些未明确因素导致原始因素风险模型的建模误差。该方法可用于各种不同因素风险模型，如基本面，统计学和宏观风险模型，以及各种证券，如股票，国际股票，复合材料，交易所交易基金（ETF）等。，等等，货币和固定收益。与特定投资组合相关的因素风险模型中的建模误差相关风险被识别和量化。使用因子风险模型估计风险或主动风险时，可以利用与建模误差相关的风险的知识，或者通过均值方差优化或利用因子风险模型的其他投资组合构建策略和程序来构建最优投资组合。

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