Distinguishing between short and long range dependence: Finite sample properties of rescaled range and modified rescaled range
Mostly used estimators of Hurst exponent for detection of long-range dependence are biased by presence of short-range dependence in the underlying time series. We present confidence intervals estimates for rescaled range and modified rescaled range. We show that the difference in expected values and confidence intervals enables us to use both methods together to clearly distinguish between the two types of processes. The estimates are further applied on Dow Jones Industrial Average between 1944 and 2009 and show that returns do not show any long-range dependence whereas volatility shows both short-range and long-range dependence in the underlying process.
|Date of creation:||01 Jul 2009|
|Date of revision:|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chin, Wencheong, 2008. "Spurious long-range dependence: evidence from Malaysian equity markets," MPRA Paper 7914, University Library of Munich, Germany.
- Berg, Lennart & Lyhagen, Johan, 1996. "Short and Long Run Dependence in Swedish Stock Returns," Working Paper Series 1996:19, Uppsala University, Department of Economics.
- Andreas S. Andreou & George A. Zombanakis, 2006. "Computational Intelligence in Exchange-Rate Forecasting," Working Papers 49, Bank of Greece.
- John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000.
"Long memory in the Greek stock market,"
Applied Financial Economics,
Taylor & Francis Journals, vol. 10(2), pages 177-184.
- Grech, D & Mazur, Z, 2004. "Can one make any crash prediction in finance using the local Hurst exponent idea?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 133-145.
- T. Di Matteo & T. Aste & Michel M. Dacorogna, 2005.
"Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development,"
- Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
- T. Di Matteo & T. Aste & M. M. Dacorogna, 2004. "Long term memories of developed and emerging markets: using the scaling analysis to characterize their stage of development," Papers cond-mat/0403681, arXiv.org.
- Thomas Lux, 2007.
"Application of Statistical Physics in Finance and Economics,"
wp07-09, Warwick Business School, Finance Group.
- Thomas Lux, 2008. "Applications of Statistical Physics in Finance and Economics," Kiel Working Papers 1425, Kiel Institute for the World Economy.
- Lux, Thomas, 2007. "Applications of statistical physics in finance and economics," Economics Working Papers 2007,05, Christian-Albrechts-University of Kiel, Department of Economics.
- Rafal Weron, 2001.
"Estimating long range dependence: finite sample properties and confidence intervals,"
HSC Research Reports
HSC/01/03, Hugo Steinhaus Center, Wroclaw University of Technology.
- Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
- T. Di Matteo, 2007. "Multi-scaling in finance," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 21-36.
- Lo, Andrew W. (Andrew Wen-Chuan), 1989.
"Long-term memory in stock market prices,"
3014-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Czarnecki, Łukasz & Grech, Dariusz & Pamuła, Grzegorz, 2008. "Comparison study of global and local approaches describing critical phenomena on the Polish stock exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6801-6811.
- Matos, José A.O. & Gama, Sílvio M.A. & Ruskin, Heather J. & Sharkasi, Adel Al & Crane, Martin, 2008. "Time and scale Hurst exponent analysis for financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3910-3915.
- Fabrizio Lillo & J. Doyne Farmer, 2003.
"The long memory of the efficient market,"
cond-mat/0311053, arXiv.org, revised Jul 2004.
- Paul Eitelman & Justin Vitanza, 2008. "A non-random walk revisited: short- and long-term memory in asset prices," International Finance Discussion Papers 956, Board of Governors of the Federal Reserve System (U.S.).
- Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo & Fernandez-Anaya, Guillermo, 2008. "Time-varying Hurst exponent for US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6159-6169.
- Couillard, Michel & Davison, Matt, 2005. "A comment on measuring the Hurst exponent of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 404-418.
- R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
- Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:16424. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.