Long-term memory and volatility clustering in high-frequency price changes

Gabjin Oh, Seunghwan Kim and Cheoljun Eom

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 5, 1247-1254

Abstract: We studied the long-term memory in diverse stock market indices and foreign exchange rates using Detrended Fluctuation Analysis (DFA). For all high-frequency market data studied, no significant long-term memory property was detected in the return series, while a strong long-term memory property was found in the volatility time series. The possible causes of the long-term memory property were investigated using the return data filtered by the AR(1) model, reflecting the short-term memory property, the GARCH(1,1) model, reflecting the volatility clustering property, and the FIGARCH model, reflecting the long-term memory property of the volatility time series. The memory effect in the AR(1) filtered return and volatility time series remained unchanged, while the long-term memory property diminished significantly in the volatility series of the GARCH(1,1) filtered data. Notably, there is no long-term memory property, when we eliminate the long-term memory property of volatility by the FIGARCH model. For all data used, although the Hurst exponents of the volatility time series changed considerably over time, those of the time series with the volatility clustering effect removed diminish significantly. Our results imply that the long-term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series.

Keywords: Long-term memory; Volatility clustering; GARCH (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (37)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:5:p:1247-1254

DOI: 10.1016/j.physa.2007.08.061

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