Multiple break detection in the correlation structure of random variables

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• 作者：Pedro Galeano^a ; ^{pedro.galeano@uc3m.es" class="auth_mail} ; Dominik Wied^b ; ¹ ; ^{wied@statistik.tu-dortmund.de" class="auth_mail}
• 关键词：Binary segmentation ; Correlations ; CUSUM statistics ; Financial returns ; Multiple change point detection
• 刊名：Computational Statistics and Data Analysis
• 出版年：August 2014
• 年：2014
• 卷：76
• 期：Complete
• 页码：262-282
• 全文大小：2417 K

文摘

Correlations between random variables play an important role in applications, e.g. in financial analysis. More precisely, accurate estimates of the correlation between financial returns are crucial in portfolio management. In particular, in periods of financial crisis, extreme movements in asset prices are found to be more highly correlated than small movements. It is precisely under these conditions that investors are extremely concerned about changes on correlations. A binary segmentation procedure to detect the number and position of multiple change points in the correlation structure of random variables is proposed. The procedure assumes that expectations and variances are constant and that there are sudden shifts in the correlations. It is shown analytically that the proposed algorithm asymptotically gives the correct number of change points and the change points are consistently estimated. It is also shown by simulation studies and by an empirical application that the algorithm yields reasonable results.