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Chocs exogènes et non linéarités dans les séries boursières: Application à la modélisation non paramétrique du cours de l'action Orange
[Exogenous Shocks and nonlinearity in the stock exchange series: Application to the nonparametric modelling of stock exchange Orange prices]

Author

Listed:
  • CHIKHI, Mohamed

Abstract

Cet article vise à analyser le comportement cyclique de la série du cours de l'action Orange du 03/01/2000 à 02/02/2017 par la recherche de la non linéarité à travers d'une classe de modèles non paramétriques hétéroscédastiques, notée NAR-ARCH. L'identification des modèles non paramétriques nécessite une sélection rigoureuse des coefficients de Markov et le choix de la fenêtre qui détermine le degré de lissage de l’estimateur. This paper aims to analyze the cyclical behavior of stock exchange Orange prices from 01/03/2000 to 02/02/2017 by the research of nonlinearities through a class of heteroscedastic non parametric models. The identification of non parametric models requires the selection of the Markov coefficients and the choice of bandwidth, which determines the degree of estimator’s smoothing.

Suggested Citation

  • CHIKHI, Mohamed, 2017. "Chocs exogènes et non linéarités dans les séries boursières: Application à la modélisation non paramétrique du cours de l'action Orange [Exogenous Shocks and nonlinearity in the stock exchange seri," MPRA Paper 76691, University Library of Munich, Germany, revised 2017.
    • Handle: RePEc:pra:mprapa:76691
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    References listed on IDEAS

    as
    1. Mohamed Chikhi & Claude Diebolt, 2009. "The Reichsbank: a nonparametric modelling of historical time series," Applied Economics Letters, Taylor & Francis Journals, vol. 16(14), pages 1409-1414.
    2. Ullah, Aman, 1988. "Nonparametric Estimation and Hypothesis Testing in Econometric Models," Empirical Economics, Springer, vol. 13(3/4), pages 223-249.
    3. Wolfgang Härdle & Helmut Lütkepohl & Rong Chen, 1997. "A Review of Nonparametric Time Series Analysis," International Statistical Review, International Statistical Institute, vol. 65(1), pages 49-72, April.
    4. Tschernig, R., 1996. "Nonlinearities in German Unemployment Rates: A Nonparametric Analysis," SFB 373 Discussion Papers 1996,45, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    6. Yang, Lijian & Tschernig, Rolf, 2002. "Non- And Semiparametric Identification Of Seasonal Nonlinear Autoregression Models," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1408-1448, December.
    7. Rolf Tschernig & Lijian Yang, 2000. "Nonparametric Lag Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(4), pages 457-487, July.
    8. Mohamed Chikhi & Claude Diebolt, 2010. "Nonparametric analysis of financial time series by the Kernel methodology," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(5), pages 865-880, August.
    9. L. Yang & R. Tschernig, 1999. "Multivariate bandwidth selection for local linear regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 793-815.
    10. Chiu, Shean-Tsong, 1989. "Bandwidth selection for kernel estimate with correlated noise," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 347-354, September.
    11. P. M. Robinson, 1983. "Nonparametric Estimators For Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(3), pages 185-207, May.
    12. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    13. Härdle, Wolfgang & Chen, R., 1995. "Nonparametric Time Series Analysis, a selectiv review with examples," SFB 373 Discussion Papers 1995,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Lijian Yang & Wolfgang Hardle & Jens Nielsen, 1999. "Nonparametric Autoregression with Multiplicative Volatility and Additive mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 579-604, September.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    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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