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

Journal of Economics and Financial Analysis

Ali Bendob¹  Mohamed Chikhi² 

[1] University of Ain-Temouchent;University of Ouargla

关键词: Final Prediction Error;    Kernel;    Bandwidth;    Conditional Heteroscedastic Functional Autoregressive Process;    Orange Stock Price;    Forecasts.;   

DOI  :  10.1991/jefa.v2i2.a20

学科分类：社会科学、人文和艺术（综合）

来源: Tripal Publishing House

【 摘 要 】

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.

【 授权许可】

CC BY-NC-ND