Abstract
Predicting volatility in stock market price indices is a major economic problem. The idea of forecasting time series is that the patterns associated with past values in a data series can be used to project future values. The study of volatility can be applied to solving these economic problems, because volatility allows measuring the risk of asset portfolios, since it shows the behavior of the variation of asset prices. In order to be able to predict effectively the future behavior of a time series, it is necessary to know the attributes of the series with the correct prediction method and thus to be able to define training patterns. The accurate selection of the attributes evaluated in a time series defines the impact on prediction accuracy. In this work the study of kurtosis and the comparison between different ARIMA methods for the solution of time series of the Mexican Stock Exchange and the Makridakis contests are shown.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Keck, A. Raubold, A. Truppia, Forecasting international trade: a time series approach. OECD J. J. Bus. Cycle Meas. Anal. 2009(2), 157 (2009)
F. Black, M. Scholes, The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)
E.F. Fama, Efficient capital markets: a review of theory and empirical work. J. Finance 25(2), 383–417 (1970)
R.J. Shiller, From efficient markets theory to behavioral finance. J. Econ. Perspect. 17(1), 83–104 (2003)
J.Y. Campbell, A.W.-C. Lo, A.C. MacKinlay, in The Econometrics of Financial Markets (Princeton University Press, New Jersey, 1997)
P.J. Brockwell, R.A. Davis, in Introduction to Time Series and Forecasting (Springer, Berlin, 2016)
J. Frausto-Solis, E. Pita, J. Lagunas, Short-term streamflow forecasting: ARIMA vs. Neural Networks, in American Conference on Applied Mathematics (MATH’08), Harvard, Massachusetts, USA, 2008, pp. 402–407
G.E.P. Box, G.M. Jenkins, in Time Series Analysis: Forecasting and Control, 1976. (1970) ISBN 0-8162-1104-3
Acknowledgements
The authors would like to acknowledge the Consejo Nacional de Ciencia y Tecnología (CONACYT). Besides, they acknowledge the Laboratorio Nacional de Tecnologías de la Información (LaNTI) of the Instituto Tecnológico de Ciudad Madero for the access to the cluster. Also, Javier Alberto Rangel González thanks the scholarship 429340 received from CONACYT in his Ph.D.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Rangel-González, J.A., Frausto-Solis, J., Javier González-Barbosa, J., Pazos-Rangel, R.A., Fraire-Huacuja, H.J. (2018). Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-71008-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71007-5
Online ISBN: 978-3-319-71008-2
eBook Packages: EngineeringEngineering (R0)