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The Effects of Additive Outliers and Measurement Errors when Testing for Structural Breaks in Variance

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  • Paulo M. M. Rodrigues
  • Antonio Rubia

Abstract

This paper discusses the asymptotic and finite-sample properties of CUSUM-based tests for detecting structural breaks in volatility in the presence of stochastic contamination, such as additive outliers or measurement errors. This analysis is particularly relevant for financial data, on which these tests are commonly used to detect variance breaks. In particular, we focus on the tests by Inclán and Tiao [IT] (1994) and Kokoszka and Leipus [KL] (1998, 2000), which have been intensively used in the applied literature. Our results are extensible to related procedures. We show that the asymptotic distribution of the IT test can largely be affected by sample contamination, whereas the distribution of the KL test remains invariant. Furthermore, the break-point estimator of the KL test renders consistent estimates. In spite of the good large-sample properties of this test, large additive outliers tend to generate power distortions or wrong break-date estimates in small samples.

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Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 73 (2011)
Issue (Month): 4 (08)
Pages: 449-468

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Handle: RePEc:bla:obuest:v:73:y:2011:i:4:p:449-468

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References

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  1. Ai Deng & Pierre Perron, 2006. "The Limit Distribution of the CUSUM of Squares Test Under General Mixing Conditions," Boston University - Department of Economics - Working Papers Series, Boston University - Department of Economics wp2006-004, Boston University - Department of Economics.
  2. Ai Deng & Pierre Perron, 2005. "A Non-local Perspective on the Power Properties of the CUSUM and CUSUM of Squares Tests for Structural Change," Boston University - Department of Economics - Working Papers Series, Boston University - Department of Economics WP2005-047, Boston University - Department of Economics.
  3. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 290-318.
  4. Chen, Gongmeng & Choi, Yoon K. & Zhou, Yong, 2005. "Nonparametric estimation of structural change points in volatility models for time series," Journal of Econometrics, Elsevier, Elsevier, vol. 126(1), pages 79-114, May.
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Cited by:
  1. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-00952951, HAL.
  2. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
  3. Martín Saldías & Rafael Barbosa, 2013. "Option trade volume and volatility of banks’ stock returns," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department, Banco de Portugal, Economics and Research Department.
  4. Reese, Simon & Li, Yushu, 2013. "Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements," Working Papers, Lund University, Department of Economics 2013:36, Lund University, Department of Economics.
  5. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, Elsevier, vol. 43(C), pages 188-199.

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