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

(SUPSI) Scuola universitaria professionale della Svizzera italiana

O. Scaillet

Swiss Finance Institute - University of Geneva

Fabio Trojani

University of Geneva; University of Turin - Department of Statistics and Applied Mathematics; Swiss Finance Institute

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 di®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

(SUPSI) Scuola universitaria professionale della Svizzera italiana ( email )

Le Gerre
Manno, CA Canton Ticino CH-6928
Switzerland

Olivier Scaillet

Swiss Finance Institute - University of Geneva ( email )

Geneva
Switzerland

Fabio Trojani (Contact Author)

University of Geneva ( email )

Geneva, Geneva
Switzerland

University of Turin - Department of Statistics and Applied Mathematics ( email )

Piazza Arbarello, 8
Turin, I-10122
Italy

Swiss Finance Institute ( email )

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

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