IDEAS home Printed from https://ideas.repec.org/p/zbw/cauewp/1123.html
   My bibliography  Save this paper

The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting

Author

Listed:
  • Lux, Thomas

Abstract

Multi-fractal processes have been proposed as a new formalism for modeling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found to characterize virtually all financial prices. Furthermore, elementary variants of multi-fractal models are very parsimonious formalizations as they are essentially one-parameter families of stochastic processes. The aim of this paper is to provide the characteristics of a causal multi-fractal model (replacing the earlier combinatorial approaches discussed in the literature), to estimate the parameters of this model and to use these estimates in forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate its parameters consistently via GMM (Generalized Method of Moment). Simulations show that this approach leads to essentially unbiased estimates, which also have much smaller root mean squared errors than those obtained from the traditional ?scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important financial assets. Comparing the multi-fractal forecasts with those derived from GARCH and FIGARCH models yields results in favor of the new model: multi-fractal forecasts dominate all other forecasts in one out of four cases considered, while in the remaining cases they are head to head with one or more of their competitors.

Suggested Citation

  • Lux, Thomas, 2003. "The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting," Economics Working Papers 2003-13, Christian-Albrechts-University of Kiel, Department of Economics.
  • Handle: RePEc:zbw:cauewp:1123
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/3031/1/EWP-2003-13.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    3. 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.
    4. Andersen, Torben G & Bollerslev, Tim, 1997. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," Journal of Finance, American Finance Association, vol. 52(3), pages 975-1005, July.
    5. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265.
    6. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    7. Thomas Lux, 1996. "Long-term stochastic dependence in financial prices: evidence from the German stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 3(11), pages 701-706.
    8. T. Lux, 2001. "Turbulence in financial markets: the surprising explanatory power of simple cascade models," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 632-640.
    9. Calvet, Laurent & Fisher, Adlai, 2001. "Forecasting multifractal volatility," Journal of Econometrics, Elsevier, vol. 105(1), pages 27-58, November.
    10. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    11. Brockwell, P. J. & Dahlhaus, R., 2004. "Generalized Levinson-Durbin and Burg algorithms," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 129-149.
    12. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    13. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    14. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
    15. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    16. Terence Mills, 1997. "Stylized facts on the temporal and distributional properties of daily FT-SE returns," Applied Financial Economics, Taylor & Francis Journals, vol. 7(6), pages 599-604.
    17. Goetzmann, William Nelson, 1993. "Patterns in Three Centuries of Stock Market Prices," The Journal of Business, University of Chicago Press, vol. 66(2), pages 249-270, April.
    18. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    19. J-F. Muzy & D. Sornette & J. delour & A. Arneodo, 2001. "Multifractal returns and hierarchical portfolio theory," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 131-148.
    20. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    21. 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.
    22. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    23. Adlai Fisher & Laurent Calvet & Benoit Mandelbrot, 1997. "Multifractality of Deutschemark/US Dollar Exchange Rates," Cowles Foundation Discussion Papers 1166, Cowles Foundation for Research in Economics, Yale University.
    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. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    2. Oświe¸cimka, P. & Kwapień, J. & Drożdż, S., 2005. "Multifractality in the stock market: price increments versus waiting times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 626-638.
    3. Kwapień, J. & Drożdż, S. & Oświe¸cimka, P., 2006. "The bulk of the stock market correlation matrix is not pure noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 589-606.
    4. Lux, Thomas & Kaizoji, Taisei, 2004. "Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models," Economics Working Papers 2004-05, Christian-Albrechts-University of Kiel, Department of Economics.
    5. Davies, Paul Lyndon, 2006. "Long range financial data and model choice," Technical Reports 2006,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. G.-F. Gu & W.-X. Zhou, 2009. "On the probability distribution of stock returns in the Mike-Farmer model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(4), pages 585-592, February.
    7. Pablo Su'arez-Garc'ia & David G'omez-Ullate, 2013. "Multifractality and long memory of a financial index," Papers 1306.0490, arXiv.org.
    8. Indranil Mukherjee & Amitava Sarkar, 2011. "Complexity, Financial Markets and their Scaling Laws," DEGIT Conference Papers c016_008, DEGIT, Dynamics, Economic Growth, and International Trade.
    9. Kwapień, J. & Oświe¸cimka, P. & Drożdż, S., 2005. "Components of multifractality in high-frequency stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 466-474.
    10. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    11. Suárez-García, Pablo & Gómez-Ullate, David, 2014. "Multifractality and long memory of a financial index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 226-234.
    12. E. Bacry & A. Kozhemyak & J. F. Muzy, 2011. "Log-normal continuous cascade model of asset returns: aggregation properties and estimation," Quantitative Finance, Taylor & Francis Journals, vol. 13(5), pages 795-818, October.
    13. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    14. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.
    15. Thomas Lux, 2004. "Detecting Multifractal Properties In Asset Returns: The Failure Of The "Scaling Estimator"," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 481-491.
    16. Eisler, Z. & Kertész, J., 2004. "Multifractal model of asset returns with leverage effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 603-622.
    17. Amir Safari & Detlef Seese, 2009. "Non-parametric estimation of a multiscale CHARN model using SVR," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 105-121.

    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. Lux, Thomas, 2008. "The Markov-Switching Multifractal Model of Asset Returns: GMM Estimation and Linear Forecasting of Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 194-210, April.
    2. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy (IfW Kiel).
    3. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    4. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    5. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy (IfW Kiel).
    6. Lux, Thomas & Kaizoji, Taisei, 2004. "Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models," Economics Working Papers 2004-05, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    8. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy (IfW Kiel).
    9. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    10. Nasr, Adnen Ben & Lux, Thomas & Ajmi, Ahdi Noomen & Gupta, Rangan, 2016. "Forecasting the volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. regime switching," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 559-571.
    11. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    12. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
    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. Rehim Kili, 2004. "On the long memory properties of emerging capital markets: evidence from Istanbul stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 915-922.
    15. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    16. John Cotter & Simon Stevenson, 2008. "Modeling Long Memory in REITs," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(3), pages 533-554, September.
    17. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy (IfW Kiel).
    18. Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
    19. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    20. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.

    More about this item

    Keywords

    multi-fractality; financial volatility; forecasting;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:zbw:cauewp:1123. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vakiede.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.