In this paper we propose a method to locate multiple structural breaks in financial time series that accounts for the interval structure of these series as well as for the presence of outliers. For each time unit, the upper and lower bound of the intervals depend on the closing value. Then, to locate the break dates, a robust exponential based distance, that is able to neutralize the impact of outlier, is employed in the framework of Atheoretical Regression Trees. An empirical application to the prices of an asset shows the usefulness of the proposed procedure.

Robust Atheoretical Regression Tree to detect structural breaks in financial time series

CAPPELLI, CARMELA;DI IORIO, FRANCESCA
2016

Abstract

In this paper we propose a method to locate multiple structural breaks in financial time series that accounts for the interval structure of these series as well as for the presence of outliers. For each time unit, the upper and lower bound of the intervals depend on the closing value. Then, to locate the break dates, a robust exponential based distance, that is able to neutralize the impact of outlier, is employed in the framework of Atheoretical Regression Trees. An empirical application to the prices of an asset shows the usefulness of the proposed procedure.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/634561
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