IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/41407.html
   My bibliography  Save this paper

Could Investors’ Expectations Explain Temporal Variations in Hurst’s Exponent, Loci of Multifractal Spectra, and Statistical Prediction Errors? The Case of the S&P 500 Index

Author

Listed:
  • Dominique, C-Rene
  • Rivera-Solis, Luis Eduardo

Abstract

Over the periods 1998-2002 and 2009-2011, the S&P-500 Index went from persistence to anti-persistence mode, as measured by the Hurst index H. To uncover the reasons that characterize such a change, this paper uses a simple method that consists in treating quasi self-similar segments of the Index as initiators, and then finding appropriate generators with two intervals each to asymptotically model the strange attractor. The multifractal formalism shows that the change in persistence implies a corresponding change in the multifractal spectrum, and an enlargement of the invariant equilibrium set, making a market crash more likely, most probably due to a collapse of investors’ expectations. This also means that all statistical predictions made in one mode would have been off by an amount proportional to change in any element of the generalized set of dimensions in the other.

Suggested Citation

  • Dominique, C-Rene & Rivera-Solis, Luis Eduardo, 2012. "Could Investors’ Expectations Explain Temporal Variations in Hurst’s Exponent, Loci of Multifractal Spectra, and Statistical Prediction Errors? The Case of the S&P 500 Index," MPRA Paper 41407, University Library of Munich, Germany, revised 26 Feb 2012.
    • Handle: RePEc:pra:mprapa:41407
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/41407/1/MPRA_paper_41407.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    2. Greene, Myron T. & Fielitz, Bruce D., 1977. "Long-term dependence in common stock returns," Journal of Financial Economics, Elsevier, vol. 4(3), pages 339-349, May.
    3. Medio,Alfredo & Gallo,Giampaolo, 1995. "Chaotic Dynamics," Cambridge Books, Cambridge University Press, number 9780521484619, November.
    4. 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.
    5. Dominique, C-René & Rivera-Solis, Luis Eduardo, 2011. "Mixed fractional Brownian motion, short and long-term Dependence and economic conditions: the case of the S&P-500 Index," MPRA Paper 34860, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dominique, C-Rene & Rivera-Solis, Luis Eduardo, 2012. "The dynamics of market share’s growth and competition in quadratic mappings," MPRA Paper 43652, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominique, C-Rene & Rivera-Solis, Luis Eduardo, 2012. "Short-term Dependence in Time Series as an Index of Complexity: Example from the S&P-500 Index," MPRA Paper 41408, University Library of Munich, Germany.
    2. Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    3. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    4. Dominique, C-Rene, 2018. "Assessing the Entropies of the Feigenbaum Strange Attractor and the S&P-500 Index as Factors Driving the Production of Information in Market Economies," MPRA Paper 89873, University Library of Munich, Germany, revised 05 Nov 2018.
    5. Schadner, Wolfgang, 2021. "On the persistence of market sentiment: A multifractal fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    6. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    7. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    8. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    9. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    10. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    11. Dufour, Jean-Marie & García, René, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    12. Erhard Reschenhofer & Manveer K. Mangat, 2021. "Fast computation and practical use of amplitudes at non-Fourier frequencies," Computational Statistics, Springer, vol. 36(3), pages 1755-1773, September.
    13. Krämer, Walter & Sibbertsen, Philipp & Kleiber, Christian, 2001. "Long memory vs. structural change in financial time series," Technical Reports 2001,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    14. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    15. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    16. van Eyden, Reneé & Gupta, Rangan & Nielsen, Joshua & Bouri, Elie, 2023. "Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    17. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    18. Christos Christodoulou-Volos & Fotios Siokis, 2006. "Long range dependence in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1331-1338.
    19. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    20. Harvey, Andrew & Palumbo, Dario, 2023. "Score-driven models for realized volatility," Journal of Econometrics, Elsevier, vol. 237(2).

    More about this item

    Keywords

    Persistence; Strange Equilibrium sets; Scaling Exponents; Multifractal Spectra; Generalized Dimensions of order q; Statistical-prediction-error;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C0 - Mathematical and Quantitative Methods - - General
    • G00 - Financial Economics - - General - - - General
    • A10 - General Economics and Teaching - - General Economics - - - General
    • G01 - Financial Economics - - General - - - Financial Crises

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:41407. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.