IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/10674.html
   My bibliography  Save this paper

An algorithm using GARCH process , Monte-Carlo simulation and wavelets analysis for stock prediction

Author

Listed:
  • Giovanis, Eleftherios

Abstract

This paper examines and presents a simple algorithm for prediction stock written in MATLAB code. We apply it to thirty stocks of the Athens exchange stock market . We obtain the stock returns and we would like to predict, not the actual price , but the sign of stock returns. The results are very satisfying while we predict the right sign for 25 out of 30 cases or else we have a success of 83.33%. The problem with the algorithm is that we don’t have the ability to predict zero returns.

Suggested Citation

  • Giovanis, Eleftherios, 2008. "An algorithm using GARCH process , Monte-Carlo simulation and wavelets analysis for stock prediction," MPRA Paper 10674, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:10674
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/10674/1/MPRA_paper_10674.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    Full references (including those not matched with items on IDEAS)

    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. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
    2. Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
    3. Ginker, Tim & Lieberman, Offer, 2017. "Robustness of binary choice models to conditional heteroscedasticity," Economics Letters, Elsevier, vol. 150(C), pages 130-134.
    4. Chin-Shien Lin & Haider Ali Khan & Chi-Chung Huang, 2002. "Can the neuro fuzzy model predict stock indexes better than its rivals?," CIRJE F-Series CIRJE-F-165, CIRJE, Faculty of Economics, University of Tokyo.
    5. Md. Zahangir Alam & Md. Noman Siddikee & Md. Masukujjaman, 2013. "Forecasting Volatility of Stock Indices with ARCH Model," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(2), pages 126-143, April.
    6. Leonard Mushunje & Maxwell Mashasha & Edina Chandiwana, 2023. "Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)," Papers 2311.10935, arXiv.org.
    7. Rasika Yatigammana & Shelton Peiris & Richard Gerlach & David Edmund Allen, 2018. "Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants," Risks, MDPI, vol. 6(2), pages 1-22, May.
    8. Beran, Jan & Feng, Yuanhua, 1999. "Local Polynomial Estimation with a FARIMA-GARCH Error Process," CoFE Discussion Papers 99/08, University of Konstanz, Center of Finance and Econometrics (CoFE).
    9. Corbet, Shaen & Larkin, Charles & McMullan, Caroline, 2020. "The impact of industrial incidents on stock market volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    10. Cho, Guedae & Kim, MinKyoung & Koo, Won W., 2003. "Relative Agricultural Price Changes In Different Time Horizons," 2003 Annual meeting, July 27-30, Montreal, Canada 22249, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    12. Umar, Muhammad & Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Furqan, Mehreen, 2023. "Asymmetric volatility structure of equity returns: Evidence from an emerging market," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 330-336.
    13. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    14. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    15. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    16. Tomanova, Lucie, 2013. "Exchange Rate Volatility and the Foreign Trade in CEEC," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 267, Ekonomik Yaklasim Association.
    17. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    18. Jumah, Adusei & Kunst, Robert M., 2001. "The Effects of Exchange-Rate Exposures on Equity Asset Markets," Economics Series 94, Institute for Advanced Studies.
    19. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    20. Gruener Hans Peter & Hayo Bernd & Hefeker Carsten, 2009. "Unions, Wage Setting and Monetary Policy Uncertainty," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-25, October.

    More about this item

    Keywords

    GARCH˙ wavelets˙ forecasting˙ Monte-Carlo˙ wavelet discrete transformation;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    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:10674. 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.