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Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching

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  • Lux, Thomas
  • Kaizoji, Taisei

Abstract

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal model) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have quite a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models. --

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Bibliographic Info

Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2006,13.

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Date of creation: 2006
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Handle: RePEc:zbw:cauewp:5160

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Keywords: Forecasting; Long memory models; Volume; Volatility;

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  1. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, Elsevier, vol. 76(1), pages 59-64, June.
  2. Christopher J. Neely & Paul A. Weller, 2002. "Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics," Review, Federal Reserve Bank of St. Louis, issue May, pages 43-54.
  3. Man, K. S., 2003. "Long memory time series and short term forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 19(3), pages 477-491.
  4. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
  5. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 74(1), pages 3-30, September.
  6. I.N. Lobato & N.E. Savin, 1996. "Real and Spurious Long Memory Properties of Stock Market Data," Econometrics, EconWPA 9605004, EconWPA, revised 26 Sep 1996.
  7. Laurent Calvet & Adlai Fisher & Benoit Mandelbrot, 1999. "A Multifractal Model of Assets Returns," New York University, Leonard N. Stern School Finance Department Working Paper Seires, New York University, Leonard N. Stern School of Business- 99-072, New York University, Leonard N. Stern School of Business-.
  8. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, Cambridge University Press, vol. 11(S1), pages 80-101, November.
  9. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
  10. repec:att:wimass:9317 is not listed on IDEAS
  11. Granger, Clive W. J. & Terasvirta, Timo, 1999. "A simple nonlinear time series model with misleading linear properties," Economics Letters, Elsevier, Elsevier, vol. 62(2), pages 161-165, February.
  12. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, Elsevier, vol. 69(2), pages 367-391, October.
  13. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(4), pages 410-27, October.
  14. Laurent Calvet, 2000. "Forecasting Multifractal Volatility," Harvard Institute of Economic Research Working Papers 1902, Harvard - Institute of Economic Research.
  15. Laurent Calvet & Adlai Fisher & Benoit Mandelbrot, 1997. "Large Deviations and the Distribution of Price Changes," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1165, Cowles Foundation for Research in Economics, Yale University.
  16. 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, Elsevier, vol. 1(1), pages 83-106, June.
  17. Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, Elsevier, vol. 14(2-3), pages 399-421, August.
  18. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 17(1), pages 9-21, January.
  19. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, Elsevier, vol. 4(1), pages 17-46, January.
  20. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, Elsevier, vol. 105(1), pages 131-159, November.
  21. 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.
  22. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 8(2), pages 225-34, April.
  23. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, Elsevier, vol. 20(3), pages 419-438, April.
  24. Adlai Fisher & Laurent Calvet & Benoit Mandelbrot, 1997. "Multifractality of Deutschemark/US Dollar Exchange Rates," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1166, Cowles Foundation for Research in Economics, Yale University.
  25. Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 53(4), pages 671-90, August.
  26. Ray, Bonnie K & Tsay, Ruey S, 2000. "Long-Range Dependence in Daily Stock Volatilities," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(2), pages 254-62, April.
  27. Gilles Zumbach, 2004. "Volatility processes and volatility forecast with long memory," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 4(1), pages 70-86.
  28. B. B. Mandelbrot, 2001. "Stochastic volatility, power laws and long memory," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 1(6), pages 558-559.
  29. 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.
  30. B. LeBaron, 2001. "Stochastic volatility as a simple generator of apparent financial power laws and long memory," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 1(6), pages 621-631.
  31. Tse, Y. K., 1991. "Stock returns volatility in the Tokyo stock exchange," Japan and the World Economy, Elsevier, Elsevier, vol. 3(3), pages 285-298, November.
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Citations

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Cited by:
  1. Tansuchat, R. & Chang, C-L. & McAleer, M.J., 2009. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns," Econometric Institute Research Papers EI 2009-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Julien Idier, 2011. "Long-term vs. short-term comovements in stock markets: the use of Markov-switching multifractal models," The European Journal of Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 17(1), pages 27-48.
  3. Adnen Ben Nasr & Thomas Lux & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long Memory vs. Regime Switching," Working Papers 2014-236, Department of Research, Ipag Business School.
  4. Elliott, Robert J. & Siu, Tak Kuen & Badescu, Alexandru, 2011. "On pricing and hedging options in regime-switching models with feedback effect," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(5), pages 694-713, May.
  5. Axel Groß-Klußmann & Nikolaus Hautsch, 2011. "Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models," SFB 649 Discussion Papers SFB649DP2011-044, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  6. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 387(21), pages 5189-5196.
  7. Schmitt, Noemi & Westerhoff, Frank, 2013. "Speculative behavior and the dynamics of interacting stock markets," BERG Working Paper Series 90, Bamberg University, Bamberg Economic Research Group.
  8. Thomas Lux & Leonardo Morales-Arias & Cristina Sattarhoff, 2011. "A Markov-switching Multifractal Approach to Forecasting Realized Volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy.
  9. Ruipeng Liu & Thomas Lux, 2010. "Flexible and Robust Modelling of Volatility Comovements: A Comparison of Two Multifractal Models," Kiel Working Papers 1594, Kiel Institute for the World Economy.
  10. Siokis, Fotios M., 2014. "European economies in crisis: A multifractal analysis of disruptive economic events and the effects of financial assistance," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 395(C), pages 283-292.
  11. Söderberg, Jonas, 2008. "Do Macroeconomic Variables Forecast Changes in Liquidity? An Out-of-sample Study on the Order-driven Stock Markets in Scandinavia," CAFO Working Papers, Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University 2009:10, Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University.
  12. Siokis, Fotios M., 2013. "Multifractal analysis of stock exchange crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 392(5), pages 1164-1171.

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