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

Mohamed Chikhi () and Ali Bendob ()

Journal of Economics and Financial Analysis, 2018, vol. 2, issue 2, 105-120

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.

Keywords: Final Prediction Error; Kernel; Bandwidth; Conditional Heteroscedastic Functional Autoregressive Process; Orange Stock Price; Forecasts. (search for similar items in EconPapers)
JEL-codes: C14 C22 C58 G17 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:trp:01jefa:jefa0020

DOI: http://dx.doi.org/10.1991/jefa.v2i2.a20

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