Forecasting a Long Memory Process Subject to Structural Breaks

37 Pages Posted: 20 Aug 2013 Last revised: 26 Aug 2013

See all articles by Cindy S.H. Wang

Cindy S.H. Wang

Catholic University of Louvain (UCL); National Tsing Hua University - College of Technology Management

Luc Bauwens

Université catholique de Louvain

Cheng Hsiao

University of Southern California - Department of Economics; National Taiwan University; National Bureau of Economic Research (NBER)

Date Written: January 19, 2013

Abstract

We develop an easy-to-implement method for forecasting a stationary auto-regressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an auto-regressive (AR) model and suggest using an information criterion (AIC or Mallows’ Cp) to choose the order of the approximate AR model. Our method avoids the issue of estimation inaccuracy of the long memory parameter and the issue of spurious breaks in finite sample. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing prediction methods. An empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model is explained, from an econometric perspective, by our theoretical and simulation results.

Keywords: forecasting, long memory process, structural break, HAR model

JEL Classification: C22, C53

Suggested Citation

Wang, Cindy S.H. and Wang, Cindy S.H. and Bauwens, Luc and Hsiao, Cheng, Forecasting a Long Memory Process Subject to Structural Breaks (January 19, 2013). CAFE Research Paper No. 13.01, Available at SSRN: https://ssrn.com/abstract=2313227 or http://dx.doi.org/10.2139/ssrn.2313227

Cindy S.H. Wang

Catholic University of Louvain (UCL) ( email )

Place Montesquieu, 3
Louvain-la-Neuve, 1348
Belgium

National Tsing Hua University - College of Technology Management ( email )

101, Section 2 Kuang Fu Road
Hsinchu, Taiwan 300
China

Luc Bauwens

Université catholique de Louvain ( email )

CORE
34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
32 10 474321 (Phone)
32 10 474301 (Fax)

Cheng Hsiao (Contact Author)

University of Southern California - Department of Economics ( email )

3620 South Vermont Ave. Kaprielian (KAP) Hall, 300
Los Angeles, CA 90089
United States

National Taiwan University

1 Sec. 4, Roosevelt Road
Taipei, 106
Taiwan

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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