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

Autor:	Mohamed Chikhi, Ali Bendob
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Final Prediction Error Kernel Bandwidth Conditional Heteroscedastic Functional Autoregressive Process Orange Stock Price Forecasts Finance HG1-9999 Economics as a science HB71-74
Zdroj:	Journal of Economics and Financial Analysis, Vol 2, Iss 2, Pp 105-120 (2018)
Druh dokumentu:	article
ISSN:	2521-6627 2521-6619
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/caa982e8a4af443cae3ea8a93fd235da Zobrazit plný text záznamu View record in DOAJ