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1. 发明授权

US08315936B2 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错误规范
公开(公告)号：US08315936B2
公开(公告)日：2012-11-20
申请号：US12711554
申请日：2010-02-24
申请人： Robert A. Stubbs , Stefan Hans Schmieta
发明人： Robert A. Stubbs , Stefan Hans 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提高的准确度。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

US20130041848A1 Identifying and Compensating for Model Mis-Specification in Factor Risk Models 审中-公开
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：US20130041848A1
公开(公告)日：2013-02-14
申请号：US13654797
申请日：2012-10-18
申请人： Robert A. Stubbs , Stefan Hans Schmieta
发明人： Robert A. Stubbs , Stefan Hans Schmieta
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
摘要： Techniques for using factor risk models to more accurately estimate the risk or active risk of an investment portfolio are disclosed. Inherent “modeling error” in factor risk models is identified and compensated for. One or more factors are added to compensate for factors that are unspecified or unattributed in the original factor risk model and which lead to modeling error. The approach can be used with a variety of different factor risk models, and for a variety of securities. Knowledge of the 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 using factor risk models.
摘要翻译：披露了使用因子风险模型更准确地估计投资组合的风险或主动风险的技术。确定并补偿因素风险模型的固有建模误差。添加一个或多个因素来补偿原始因子风险模型中未指定或未归因的因素，并导致建模误差。该方法可用于各种不同因素风险模型，以及各种证券。在使用因子风险模型估计风险或主动风险时，或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构建最优投资组合时，可以利用与建模误差相关的风险的知识。

3. 发明授权

US07698202B2 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错误规范
公开(公告)号：US07698202B2
公开(公告)日：2010-04-13
申请号：US11668294
申请日：2007-01-29
申请人： Robert A. Stubbs , Stefan Hans Schmieta
发明人： Robert A. Stubbs , Stefan Hans 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。

4. 发明授权

US08533107B2 Returns-timing for multiple market factor risk models 有权
标题翻译：多个市场因素风险模型的回报 - 时间
公开(公告)号：US08533107B2
公开(公告)日：2013-09-10
申请号：US13503696
申请日：2011-05-19
申请人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
发明人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q40/08
摘要： Until recently, risk models have been built using low frequency data, such as weekly or monthly data. This approach has resulted in a necessary compromise between model stability for which one needs a long history of data, and model responsiveness, for which, the shorter the history, the better. Stability plus responsiveness can be achieved if one uses daily data, which allows for a large number of observations to be used in model estimation without using long out-of-date data. Daily data have other problems, however, as the differing closing times of markets worldwide may induce spurious relationships across model factors. In particular, correlations between markets may appear lower than they truly are due to a market lag effect. To address such issues, a stable, daily data-based factor risk model is described which takes account of the differing market closing times and corrects the model factor correlations and specific returns accordingly.
摘要翻译：直到最近，已经使用低频数据（例如每周或每月数据）构建风险模型。这种方法导致了模型稳定性之间的必要妥协，人们需要长期的数据历史和模型响应能力，历史越短越好。如果使用日常数据，可以实现稳定性和响应能力，这样可以在不使用长期过期数据的情况下，将大量观测数据用于模型估计。然而，每日数据还有其他问题，因为全球市场的不同关闭时间可能会导致模型因素之间的虚假关系。特别地，市场之间的相关性可能看起来比真正由于市场滞后效应低。为了解决这些问题，描述了一个稳定的基于日常数据的因素风险模型，其中考虑到不同的市场收盘时间，并相应地纠正模型因素相关性和具体回报。

5. 发明申请

US20130080310A1 Returns-Timing for Multiple Market Factor Risk Models 有权
标题翻译：多个市场因素风险模型的回报 - 时间
公开(公告)号：US20130080310A1
公开(公告)日：2013-03-28
申请号：US13503696
申请日：2011-05-19
申请人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
发明人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
IPC分类号： G06Q40/04
CPC分类号： G06Q40/06 , G06Q40/08
摘要： Until recently, risk models have been built using low frequency data, such as weekly or monthly data. This approach has resulted in a necessary compromise between model stability for which one needs a long history of data, and model responsiveness, for which, the shorter the history, the better. Stability plus responsiveness can be achieved if one uses daily data, which allows for a large number of observations to be used in model estimation without using long out-of-date data. Daily data have other problems, however, as the differing closing times of markets worldwide may induce spurious relationships across model factors. In particular, correlations between markets may appear lower than they truly are due to a market lag To address such issues, a stable, daily data-based factor risk model is described which takes account of the differing market closing times and corrects the model factor correlations and specific returns accordingly.
摘要翻译：直到最近，已经使用低频数据（例如每周或每月数据）构建风险模型。这种方法导致了模型稳定性之间的必要妥协，人们需要长期的数据历史和模型响应能力，历史越短越好。如果使用日常数据，可以实现稳定性和响应能力，这样可以在不使用长期过期数据的情况下，将大量观测数据用于模型估计。然而，每日数据还有其他问题，因为全球市场的不同关闭时间可能会导致模型因素之间的虚假关系。特别地，市场之间的相关性可能比真正由于市场滞后而显得低。为了解决这些问题，描述了稳定的基于数据的每日因素风险模型，其考虑到不同的市场收盘时间并且校正模型因素相关性并据此具体回报。

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