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; D'Urso, P.; DI IORIO, Francesca. - (2016). (Intervento presentato al convegno 48th Scientific Meeting of the Italian Statistical Society tenutosi a Università degli Studi di Salerno nel June 8, 2016 – June 10, 2016).
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
