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Nonparametric NAR-ARCH Modelling of Stock Prices by the Kernel Methodology

Author

Listed:
  • Mohamed Chikhi

    (University of Ouargla)

  • Ali Bendob

    (University of Ain-Temouchent)

Abstract

This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.

Suggested Citation

  • Mohamed Chikhi & Ali Bendob, 2018. "Nonparametric NAR-ARCH Modelling of Stock Prices by the Kernel Methodology," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 105-120.
    • Handle: RePEc:trp:01jefa:jefa0020
    DOI: http://dx.doi.org/10.1991/jefa.v2i2.a20
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    References listed on IDEAS

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    Cited by:

    1. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    2. Mitra Lal Devkota, 2018. "The Dynamic Causality Between Stock Prices And Macroeconomic Variables: Evidence From Nepal," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 5-14, December.
    3. Mohamed Chikhi & Claude Diebolt, 2019. "Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors," Working Papers of BETA 2019-06, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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    More about this item

    Keywords

    Final Prediction Error; Kernel; Bandwidth; Conditional Heteroscedastic Functional Autoregressive Process; Orange Stock Price; Forecasts.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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