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Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations

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  • Martin Rypdal
  • Espen Sirnes
  • Ola L{o}vsletten
  • Kristoffer Rypdal

Abstract

Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter {\lambda} characterising the degree of volatility clustering of asset prices. We can therefore study the time evolution of volatility clustering and test the statistical significance of this variability. By analysing data from the Oslo Stock Exchange, and comparing the results with the investment grade spread, we find that the estimates of {\lambda} are lower at times of high market uncertainty.

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  • Martin Rypdal & Espen Sirnes & Ola L{o}vsletten & Kristoffer Rypdal, 2012. "Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations," Papers 1202.4877, arXiv.org.
  • Handle: RePEc:arx:papers:1202.4877
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    References listed on IDEAS

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    1. Bacry, E. & Delour, J. & Muzy, J.F., 2001. "Modelling financial time series using multifractal random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 84-92.
    2. Ola L{o}vsletten & Martin Rypdal, 2011. "Approximated maximum likelihood estimation in multifractal random walks," Papers 1112.0105, arXiv.org, revised Feb 2012.
    3. 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.
    4. 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.
    5. 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.
    6. Rypdal, Martin & Løvsletten, Ola, 2013. "Modeling electricity spot prices using mean-reverting multifractal processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 194-207.
    7. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2008. "Multifractality in stock indexes: Fact or Fiction?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3605-3614.
    8. M. Rypdal & O. L{o}vsletten, 2011. "Multifractal modeling of short-term interest rates," Papers 1111.5265, arXiv.org.
    9. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 49-83.
    10. Sun, Xia & Chen, Huiping & Yuan, Yongzhuang & Wu, Ziqin, 2001. "Predictability of multifractal analysis of Hang Seng stock index in Hong Kong," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 473-482.
    11. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    12. Lux, Thomas, 2003. "Detecting multi-fractal properties in asset returns: The failure of the scaling estimator," Economics Working Papers 2003-14, Christian-Albrechts-University of Kiel, Department of Economics.
    13. Sun, Xia & Chen, Huiping & Wu, Ziqin & Yuan, Yongzhuang, 2001. "Multifractal analysis of Hang Seng index in Hong Kong stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 291(1), pages 553-562.
    14. Wei, Yu & Huang, Dengshi, 2005. "Multifractal analysis of SSEC in Chinese stock market: A different empirical result from Heng Seng index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 497-508.
    15. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    16. 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.
    17. 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.
    18. Martin Rypdal & Ola L{o}vsletten, 2012. "Modeling electricity spot prices using mean-reverting multifractal processes," Papers 1201.6137, arXiv.org.
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    Cited by:

    1. Kazuto Sasai & Yukio-Pegio Gunji & Tetsuo Kinoshita, 2017. "Intermittent Behavior Induced By Asynchronous Interactions In A Continuous Double Auction Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-21, March.

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