Robust Resampling Methods for Time Series

49 Pages Posted: 14 Oct 2009 Last revised: 27 Jan 2013

See all articles by Lorenzo Camponovo

Lorenzo Camponovo

University of St. Gallen

O. Scaillet

University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics

Fabio Trojani

Swiss Finance Institute; University of Geneva

Date Written: May 9, 2010

Abstract

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications. This renders inference based on standard resampling methods useless already in simple estimation and testing settings. To solve this problem, we introduce a robust fast resampling scheme that is applicable to a wide class of time series settings. Monte Carlo simulations and sensitivity analysis for the simple AR(1) model confirm the dramatic fragility of classical resampling procedures in presence of contaminations by outliers. They also show the better accuracy and efficiency of the robust resampling approach under diĀ®erent types of data constellations. A real data application to testing for stock return predictability shows that our robust approach can detect predictability structures more consistently than classical methods.

Keywords: Subsampling, bootstrap, breakdown point, robustness, time series

JEL Classification: C12, C13, C15

Suggested Citation

Camponovo, Lorenzo and Scaillet, Olivier and Trojani, Fabio, Robust Resampling Methods for Time Series (May 9, 2010). Swiss Finance Institute Research Paper No. 09-38, Available at SSRN: https://ssrn.com/abstract=1479468 or http://dx.doi.org/10.2139/ssrn.1479468

Lorenzo Camponovo

University of St. Gallen ( email )

Varnbuelstr. 14
Saint Gallen, St. Gallen CH-9000
Switzerland

Olivier Scaillet

University of Geneva GSEM and GFRI ( email )

40 Boulevard du Pont d'Arve
Geneva 4, Geneva 1211
Switzerland
+ 41 22 379 88 16 (Phone)
+41 22 389 81 04 (Fax)

HOME PAGE: http://www.scaillet.ch

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva - Research Center for Statistics

Geneva
Switzerland

Fabio Trojani (Contact Author)

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva ( email )

Geneva, Geneva
Switzerland

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