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Tests of Long Memory: A Bootstrap Approach

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  • Pilar Grau-Carles

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Abstract

Many time series in diverse fields have been found to exhibit long memory. This paper analyzes the behaviour of some of the most used tests of long memory: the R/S analysis, the modified R/S, the Geweke and Porter-Hudak (GPH) test and the detrended fluctuation analysis (DFA). Some of these tests exhibit size distortions in small samples. It is well known that the bootstrap procedure may correct this fact. Here I examine the size and power of those tests for finite samples and different distributions, such as the normal, uniform, and lognormal. In the short-memory processes such as AR, MA and ARCH and long memory ones such as ARFIMA, p-values are calculated using the post-blackening moving-block bootstrap. The Monte Carlo study suggests that the bootstrap critical values perform better. The results are applied to financial return time series. Copyright Springer Science + Business Media, Inc. 2005

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  • Pilar Grau-Carles, 2005. "Tests of Long Memory: A Bootstrap Approach," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 103-113, February.
  • Handle: RePEc:kap:compec:v:25:y:2005:i:1:p:103-113
    DOI: 10.1007/s10614-005-6277-6
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(03), pages 361-376, June.
    2. Xiao, Zhijie, 2003. "Note on bandwidth selection in testing for long range dependence," Economics Letters, Elsevier, vol. 78(1), pages 33-39, January.
    3. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    4. Andersson, Michael K. & Gredenhoff, Mikael P., 1997. "Bootstrap Testing for Fractional Integration," SSE/EFI Working Paper Series in Economics and Finance 188, Stockholm School of Economics.
    5. Kokoszka, Piotr S. & Taqqu, Murad S., 1995. "Fractional ARIMA with stable innovations," Stochastic Processes and their Applications, Elsevier, vol. 60(1), pages 19-47, November.
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    Citations

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    Cited by:

    1. Eduardo Lima & Benjamin Tabak, 2009. "Tests of Random Walk: A Comparison of Bootstrap Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 365-382, November.
    2. Mejra Festic & Alenka Kavkler & Silvo Dajcman, 2012. "Long memory in the Croatian and Hungarian stock market returns," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 30(1), pages 115-139.
    3. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    4. Lisana B. Martinez & M. Belén Guercio & Aurelio Fernandez Bariviera & Antonio Terceño, 2018. "The impact of the financial crisis on the long-range memory of European corporate bond and stock markets," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 1-15, February.
    5. Belbute, José, 2013. "Does final demand for energy in Portugal exhibit long memory?," MPRA Paper 45717, University Library of Munich, Germany.
    6. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    7. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    8. Wang, Tiansong & Wang, Jun & Zhang, Junhuan & Fang, Wen, 2011. "Voter interacting systems applied to Chinese stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2492-2506.
    9. Hull, Matthew & McGroarty, Frank, 2014. "Do emerging markets become more efficient as they develop? Long memory persistence in equity indices," Emerging Markets Review, Elsevier, vol. 18(C), pages 45-61.
    10. repec:spr:jecfin:v:42:y:2018:i:1:d:10.1007_s12197-017-9385-y is not listed on IDEAS
    11. Murphy, A. & Izzeldin, M., 2009. "Bootstrapping long memory tests: Some Monte Carlo results," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2325-2334, April.
    12. Cajueiro, Daniel O. & Tabak, Benjamin M., 2010. "Fluctuation dynamics in US interest rates and the role of monetary policy," Finance Research Letters, Elsevier, vol. 7(3), pages 163-169, September.
    13. Mikkel Bennedsen, 2016. "Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data," CREATES Research Papers 2016-21, Department of Economics and Business Economics, Aarhus University.
    14. Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
    15. Sobaci, Cihat & Sensoy, Ahmet & Erturk, Mutahhar, 2014. "Impact of short selling activity on market dynamics: Evidence from an emerging market," Journal of Financial Stability, Elsevier, vol. 15(C), pages 53-62.
    16. A. Sensoy & Benjamin Miranda Tabak, 2013. "How much random does European Union walk? A time-varying long memory analysis," Working Papers Series 342, Central Bank of Brazil, Research Department.
    17. Mikkel Bennedsen, 2016. "Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data," Papers 1608.01895, arXiv.org, revised Mar 2018.

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    Keywords

    long-memory tests; bootstrap; time series;

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