Multiple breaks detection in financial interval-valued time series

作者：

Highlights：

• We present financial time series as Interval-Valued Time Series.

• We deal with a multiple structural breaks detection problem.

• We employ a Atheoretical Regression Trees method.

• We empirically validate the proposal by detecting the breaks of a stock prices series.

摘要

•We present financial time series as Interval-Valued Time Series.•We deal with a multiple structural breaks detection problem.•We employ a Atheoretical Regression Trees method.•We empirically validate the proposal by detecting the breaks of a stock prices series.

论文关键词：Interval-valued time series,Multiple structural breaks,Atheoretical Regression Trees,Stock prices

论文评审过程：Received 26 November 2019, Revised 6 April 2020, Accepted 17 July 2020, Available online 2 August 2020, Version of Record 12 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113775