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Understanding the source of multifractality in financial markets

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  • Jozef Barunik
  • Tomaso Aste
  • Tiziana Di Matteo
  • Ruipeng Liu

Abstract

In this paper, we use the generalized Hurst exponent approach to study the multi- scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multiscaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal (MSM) model, autoregressive fractionally integrated moving average (ARFIMA) processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.

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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 1201.1535.

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Date of creation: Jan 2012
Date of revision: Jan 2012
Publication status: Published in Physica A, 391 (17), pp. 4234-4251 (2012)
Handle: RePEc:arx:papers:1201.1535

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  1. 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," Econometrics 0503004, EconWPA.
  2. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
  3. Liu, Ruipeng & Di Matteo, T. & Lux, Thomas, 2007. "True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 35-42.
  4. Kokoszka, Piotr S. & Taqqu, Murad S., 1996. "Infinite variance stable moving averages with long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 79-99, July.
  5. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," Harvard Institute of Economic Research Working Papers 1999, Harvard - Institute of Economic Research.
  6. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 49-83.
  7. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
  8. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
  9. Barunik, Jozef & Vacha, Lukas, 2010. "Monte Carlo-based tail exponent estimator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4863-4874.
  10. Laurent Calvet & Adlai Fisher, 2002. "Multifractality In Asset Returns: Theory And Evidence," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 381-406, August.
  11. Sergio Bianchi & Augusto Pianese, 2007. "Modelling stock price movements: multifractality or multifractionality?," Quantitative Finance, Taylor and Francis Journals, vol. 7(3), pages 301-319.
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Cited by:
  1. Ladislav Kristoufek & Miloslav Vosvrda, 2012. "Measuring capital market efficiency: Global and local correlations structure," Papers 1208.1298, arXiv.org.

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