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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://ojs.tripaledu.com/jefa/article/download/19/41 (application/pdf)
http://ojs.tripaledu.com/jefa/article/view/19/42 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:trp:01jefa:jefa0020

DOI: 10.1991/jefa.v2i2.a20

Access Statistics for this article

Journal of Economics and Financial Analysis is currently edited by David Simon Hall

More articles in Journal of Economics and Financial Analysis from Tripal Publishing House
Bibliographic data for series maintained by David Simon Hall ().