Long memory and data frequency in financial markets

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. ARTICLE HISTORY

Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data

“…Specifically, frequencies corresponding to the periods of 256 to 512 five-minute intervals are observed to exhibit the highest power while no strong cyclical pattern can be observed at higher frequencies. These results corroborate those of (Caporale et al 2019).…”

“…and gains, an optimal trading horizon would preferably be longer than half-day, as this could capture more fundamental trend information of the exchange-rate return processes. Our study complements the findings of (Caporale et al 2019), who documented the persistence of both returns and volatility processes of the EUR/USD and USD/JPY exchange rates at lower trading frequencies. In agreement with this paper, we concur that such evidence against random-walk behaviour implies predictability and is inconsistent with the Efficient Market Hypothesis since abnormal profits can be made using trading strategies based on trend analysis.…”

“…The conventional rescaled-range approach was first adopted by (Booth et al 1982) to account for long memory in exchange-rate data. However, a weakness of this early developed technique is that it is not robust to the short-range persistence and heteroscedasticity (Caporale et al 2019; Gil-Alana and Carcel 2020; Ouyang et al 2016; Youssef and Mokni 2020).…”

“…Concurrent with the development of this literature, another strand of research illustrates the stylised fact of longrange dependence, or long memory, in the volatility of multiple financial asset classes including currencies (e.g., Akgül and Sayyan 2008;Aloy et al 2011). In particular, the response to new information is dragged over a long period due to inefficiency in currency markets (Caporale et al 2019;Vo and Vo 2019b) leading to long-range dependence of exchange rates. However, the standard statistical techniques proposed to examine long-range dependence, such as the rescaled range method, are inadequate to capture both short-and long-range dependence (Lo 1991).…”

High and Low Prices and the Range in the European Stock Markets: A Long-Memory Approach

“…This is particularly relevant to investors aiming to design appropriate trading strategies to exploit market inefficiencies, since it sheds light on the value of d at different frequencies, profit opportunities arising when this parameter is different from 1, i.e. when prices do not follow a random walk and are therefore predictable (see Caporale et al, 2019). Further, it is conceivable that both high and low-price series are trending whilst a combination of the two is stationary; cointegration analysis sheds light on this, and in its fractional extension also allows for a J o u r n a l P r e -p r o o f very slow speed of adjustment.…”