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Detecting structural breaks in multivariate financial time series: evidence from hedge fund investment strategies

机译:检测多元金融时间序列中的结构性断裂:对冲基金投资策略的证据

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This paper extends the class of asset-based style factor models with multiple structural breaks to the multivariate setting. We propose a model that allows for the presence of common breaks in a system of factor models for individual hedge fund investment strategies, which share common investment characteristics. We develop a Bayesian approach to inference for the unknown number and positions of the structural breaks, based on a set of filtering recursions similar to those of the forward-backward algorithm. Furthermore, we identify relevant risk factors, common among the series of hedge funds, using a Bayesian model comparison approach. We apply our method to a set of correlated hedge fund strategies, which are mainly characterized by equity related bets. Multiple common breaks are identified, consistent with well-known market events, which reveal evidence for structural changes in the risk exposures as well as in the correlation structure of the analysed series.
机译:本文将具有多个结构中断的基于资产的样式因子模型的类别扩展到多元设置。我们提出了一个模型,该模型允许在具有共同投资特征的个人对冲基金投资策略的因素模型系统中存在共同突破。我们基于类似于前向后退算法的一组过滤递归,开发了一种贝叶斯方法来推断结构断裂的未知数目和位置。此外,我们使用贝叶斯模型比较方法确定了一系列对冲基金中常见的相关风险因素。我们将我们的方法应用于一系列相关对冲基金策略,这些策略主要以与股票相关的赌注为特征。与已知的市场事件一致,确定了多个共同的突破,这些突破揭示了风险敞口以及所分析系列的相关结构的结构变化的证据。

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