Long Memory and Data Frequency in Financial Markets

Guglielmo Maria Caporale, Luis Gil-Alana and Alex Plastun

No 6396, CESifo Working Paper Series from CESifo

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 (search for similar items in EconPapers)
JEL-codes: C22 G12 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Long Memory and Data Frequency in Financial Markets (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_6396

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