Long memory and data frequency in financial markets

Caporale, G.M. (Guglielmo M.); Gil-Alana, L.A. (Luis A.); Plastun, A. (Alex)

Authors:

Caporale, G.M. (Guglielmo M.)

Gil-Alana, L.A. (Luis A.)

Plastun, A. (Alex)

Keywords:

Persistence
Long memory
R/S analysis
Fractional integration

Issue Date:

2019

Publisher:

Informa UK Limited

ISSN:

0094-9655

Note:

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Citation:

Caporale, G.M. (Guglielmo M.); Gil-Alana, L.A. (Luis A.); Plastun, A. (Alex). "Long memory and data frequency in financial markets". Journal of Statistical Computation and Simulation. 89 (10), 2019, 1763 - 1779

Abstract

This paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory approaches (R/S analysis and fractional integration) for robustness purposes. The results indicate that persistence is higher at lower frequencies, for both returns and their volatility. This is true of the stock markets (both developed and emerging) and partially of the FOREX and commodity markets examined. Such evidence against the random walk behaviour implies predictability and is inconsistent with the Efficient Market Hypothesis (EMH), since abnormal profits can be made using trading strategies based on trend analysis.

URI:

https://hdl.handle.net/10171/62700

DOI:

10.1080/00949655.2019.1599377

Appears in Collections:

DA - Economía - Artículo de revista

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