This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctuation analysis (DFA) using linear and quadratic trends and the Geweke Porter-Hudak methods are applied to detect the presence of long-memory or persistence. We find that the daily returns exhibit a small amount of long memory and that the quadratic trend used in the DFA overestimates the value of the Hurst exponent. These findings are corroborated by the use of the Geweke Porter-Hudak method wherein the Hurst exponent is close to the DFA using the linear trend.
Rege, S., & Martín, S. G. (2011). Portuguese stock market: A long-memory process?. Business: Theory and Practice, 12(1), 75-84. https://doi.org/10.3846/btp.2011.08
Authors who publish with this journal agree to the following terms
that this article contains no violation of any existing copyright or other third party right or any material of a libelous, confidential, or otherwise unlawful nature, and that I will indemnify and keep indemnified the Editor and THE PUBLISHER against all claims and expenses (including legal costs and expenses) arising from any breach of this warranty and the other warranties on my behalf in this agreement;
that I have obtained permission for and acknowledged the source of any illustrations, diagrams or other material included in the article of which I am not the copyright owner.
on behalf of any co-authors, I agree to this work being published in Creativity Studies as Open Access, and licenced under a Creative Commons Licence, 4.0 https://creativecommons.org/licenses/by/4.0/legalcode. This licence allows for the fullest distribution and re-use of the work for the benefit of scholarly information.
For authors that are not copyright owners in the work (for example government employees), please contact VILNIUS TECH to make alternative agreements.