Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange
This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA model...
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Allameh Tabataba'i University Press
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doaj-489793e0960749748d428ec1c99c9bb72020-11-24T21:30:06ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292015-01-011233151181Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock ExchangeMohammad Donyaei Alireza DaliriKashi MansoorMohammad Javad MohagheghniaThis paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA models indicate the existence of long memory in return series. Also, suggest that long memory dynamics in the returns and volatility might be modeled by using the ARFIMA–FIGARCH model. Furthermore, results of this model shoes the strong evidence of long memory, both in conditional mean and conditional variance. In addition, the assumption of non-normality is appropriate for capturing the asymmetry and tail fatness of estimated residuals. These findings suggest that the model based on the Gaussian normality assumption may be inappropriate for modeling the long memory property. Finally, it seems that the Tehran Stock Exchange (TSE) cannot be considered an efficient market in terms of the speed of information transmission. Hence, speculative earnings could be gained via predicting stock prices. http://jims.atu.ac.ir/article_590_8785f29725fd8d19fd3fe345b4aede07.pdfLong memory; ARFIMA; FIGARCH; Skewed Student’s t-Distribution; Tehran Stock Exchange (TSE) |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Mohammad Donyaei Alireza Daliri Kashi Mansoor Mohammad Javad Mohagheghnia |
spellingShingle |
Mohammad Donyaei Alireza Daliri Kashi Mansoor Mohammad Javad Mohagheghnia Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī Long memory; ARFIMA; FIGARCH; Skewed Student’s t-Distribution; Tehran Stock Exchange (TSE) |
author_facet |
Mohammad Donyaei Alireza Daliri Kashi Mansoor Mohammad Javad Mohagheghnia |
author_sort |
Mohammad Donyaei |
title |
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange |
title_short |
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange |
title_full |
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange |
title_fullStr |
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange |
title_full_unstemmed |
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange |
title_sort |
evaluation of dual long memory properties with emphasizing the skewed and fat-tail distribution: evidence from tehran stock exchange |
publisher |
Allameh Tabataba'i University Press |
series |
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
issn |
2251-8029 |
publishDate |
2015-01-01 |
description |
This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX series. Results of the GPH, GSP and ARFIMA models indicate the existence of long memory in return series. Also, suggest that long memory dynamics in the returns and volatility might be modeled by using the ARFIMA–FIGARCH model. Furthermore, results of this model shoes the strong evidence of long memory, both in conditional mean and conditional variance. In addition, the assumption of non-normality is appropriate for capturing the asymmetry and tail fatness of estimated residuals. These findings suggest that the model based on the Gaussian normality assumption may be inappropriate for modeling the long memory property. Finally, it seems that the Tehran Stock Exchange (TSE) cannot be considered an efficient market in terms of the speed of information transmission. Hence, speculative earnings could be gained via predicting stock prices.
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topic |
Long memory; ARFIMA; FIGARCH; Skewed Student’s t-Distribution; Tehran Stock Exchange (TSE) |
url |
http://jims.atu.ac.ir/article_590_8785f29725fd8d19fd3fe345b4aede07.pdf |
work_keys_str_mv |
AT mohammaddonyaei evaluationofduallongmemorypropertieswithemphasizingtheskewedandfattaildistributionevidencefromtehranstockexchange AT alirezadaliri evaluationofduallongmemorypropertieswithemphasizingtheskewedandfattaildistributionevidencefromtehranstockexchange AT kashimansoor evaluationofduallongmemorypropertieswithemphasizingtheskewedandfattaildistributionevidencefromtehranstockexchange AT mohammadjavadmohagheghnia evaluationofduallongmemorypropertieswithemphasizingtheskewedandfattaildistributionevidencefromtehranstockexchange |
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