Long Memory and Data Frequency in Financial Markets

22 Pages Posted: 18 Apr 2017  

Guglielmo Maria Caporale

Brunel University - Centre for Empirical Finance

Luis A. Gil-Alana

University of Navarra - Department of Economics

Alex Plastun

Sumy State University

Date Written: March 14, 2017

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 behavior implies predictability and is inconsistent with the Efficient Market Hypothesis (EMH), since abnormal profits can be made using specific option trading strategies (butterfly, straddle, strangle, iron condor, etc.).

Keywords: persistence, long memory, R/S analysis, fractional integration

JEL Classification: C220, G120

Suggested Citation

Caporale, Guglielmo Maria and Gil-Alana, Luis A. and Plastun, Alex, Long Memory and Data Frequency in Financial Markets (March 14, 2017). CESifo Working Paper Series No. 6396. Available at SSRN: https://ssrn.com/abstract=2954500

Guglielmo Maria Caporale (Contact Author)

Brunel University - Centre for Empirical Finance ( email )

Uxbridge UB8 3PH
United Kingdom

Luis A. Gil-Alana

University of Navarra - Department of Economics ( email )

Campus de Arrosadia
Pamplona, 31006
Spain

Alex Plastun

Sumy State University ( email )

Rymskyi-Korsakov str., 2
Sumy, 40000
Ukraine

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