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Asymmetric long memory volatility in the PIIGS economies

Author

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  • Dilip Kumar
  • S. Maheswaran

Abstract

Purpose - The main purpose of this paper is to examine the asymmetry and long memory properties in the volatility of the stock indices of the PIIGS economies (Portugal, Ireland, Italy, Greece and Spain). Design/methodology/approach - The paper utilizes the wavelets approach (based on Haar, Daubechies‐4, Daubechies‐12 and Daubechies‐20 wavelets) and the GARCH class of models (namely, ARFIMA (p,d′,q)‐GARCH (1,1), IGARCH (1,1), FIGARCH (1,d,0), FIGARCH (1,d,1), EGARCH (1,1) and FIEGARCH (1,d,1)) to accomplish the desired goals. Findings - The findings provide evidence in support of the presence of long range dependence in the various proxies of volatility of the PIIGS economies. The results from the wavelet approach also support the Taylor effect in the volatility proxies. The results show that ARFIMA (p,d′,q)‐FIGARCH (1,d,0) model specification is better able to capture the long memory property of conditional volatility than the conventional GARCH and IGARCH models. In addition, the ARFIMA (p,d′,q)‐FIEGARCH (1,d,1) model is better able to capture the asymmetric long memory feature in the conditional volatility. Originality/value - This paper has both methodological and empirical originality. On the methodological side, the study applies the wavelet technique on the major proxies of volatility (squared returns, absolute returns, logarithm squared returns and the range) because the wavelet‐based estimator exhibits superior properties in modeling the behavior of the volatility of stock returns. On the empirical side, the paper finds asymmetry and long range dependence in the conditional volatility of the stock returns in PIIGS economies using the GARCH family of models.

Suggested Citation

  • Dilip Kumar & S. Maheswaran, 2013. "Asymmetric long memory volatility in the PIIGS economies," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 12(1), pages 23-43, February.
  • Handle: RePEc:eme:rafpps:v:12:y:2013:i:1:p:23-43
    DOI: 10.1108/14757701311295818
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    Citations

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    Cited by:

    1. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    2. Moussa Wajdi & Mgadmi Nidhal & Regaïeg Rym, 2018. "On the Co-movements between Exchange Rate and Stock Price from Japan: A Multivariate FIGARCH-DCC Approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(4), pages 1-4.
    3. Geoffrey Ngene & Kenneth A. Tah & Ali F. Darrat, 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, John Wiley & Sons, vol. 34(1), pages 61-73, September.
    4. Heitham Al-Hajieh, 2017. "Evaluated the Success of Fractionally Integrated-GARCH Models on Prediction Stock Market Return Volatility in Gulf Arab Stock Markets," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 200-213, July.

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