dorsal/arxiv
View SchemaLong-term memory in the Irish market (ISEQ): evidence from wavelet analysis
| Authors | Adel Sharkasi, Heather J. Ruskin, Martin Crane |
|---|---|
| Categories | |
| ArXiv ID | physics/0607182 |
| URL | https://arxiv.org/abs/physics/0607182 |
Abstract
Researchers have used many different methods to detect the possibility of long-term dependence (long memory) in stock market returns, but evidence is in general mixed. In this paper, three different tests, (namely Rescaled Range (R/S), its modified form, and the semi-parametric method (GPH)), in addition to a new approach using the discrete wavelet transform, (DWT), have been applied to the daily returns of five Irish Stock Exchange (ISEQ) indices. These methods have also been applied to the volatility measures (namely absolute and squared returns). The aim is to investigate the existence of long-term memory properties. The indices are Overall, Financial, General, Small Cap and ITEQ and the results of these approaches show that there is no evidence of long-range dependence in the returns themselves, while there is strong evidence for such dependence in the squared and absolute returns. Moreover, the discrete wavelet transform (DWT) provides additional insight on the series breakdown. In particular, in comparison to other methods, the benefit of the wavelet transform is that it provides a way to study the sensitivity of the series to increases in amplitude of fluctuations as well as changes in frequency. Finally, based on results for these methods, in particular, those for DWT of raw (or original), squared and absolute returns, it can be concluded that there is strong indication for persistence in the volatilities of the emerging stock market returns for the Irish data.
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"abstract": "Researchers have used many different methods to detect the possibility of\nlong-term dependence (long memory) in stock market returns, but evidence is in\ngeneral mixed. In this paper, three different tests, (namely Rescaled Range\n(R/S), its modified form, and the semi-parametric method (GPH)), in addition to\na new approach using the discrete wavelet transform, (DWT), have been applied\nto the daily returns of five Irish Stock Exchange (ISEQ) indices. These methods\nhave also been applied to the volatility measures (namely absolute and squared\nreturns). The aim is to investigate the existence of long-term memory\nproperties. The indices are Overall, Financial, General, Small Cap and ITEQ and\nthe results of these approaches show that there is no evidence of long-range\ndependence in the returns themselves, while there is strong evidence for such\ndependence in the squared and absolute returns. Moreover, the discrete wavelet\ntransform (DWT) provides additional insight on the series breakdown. In\nparticular, in comparison to other methods, the benefit of the wavelet\ntransform is that it provides a way to study the sensitivity of the series to\nincreases in amplitude of fluctuations as well as changes in frequency.\nFinally, based on results for these methods, in particular, those for DWT of\nraw (or original), squared and absolute returns, it can be concluded that there\nis strong indication for persistence in the volatilities of the emerging stock\nmarket returns for the Irish data.",
"arxiv_id": "physics/0607182",
"authors": [
"Adel Sharkasi",
"Heather J. Ruskin",
"Martin Crane"
],
"categories": [
"physics.data-an",
"q-fin.ST"
],
"title": "Long-term memory in the Irish market (ISEQ): evidence from wavelet analysis",
"url": "https://arxiv.org/abs/physics/0607182"
},
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