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Comparing the Leverage Effect of Different Frequencies of Stock Returns in an Emerging Market: A Case Study of Pakistan

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  • Amir Rafique

Abstract

This study compares the volatility behavior and variance structure of high (daily) and low (weekly, monthly) frequencies of data. The study used seventeen years data from 1991 to 2008 of KSE-100 index. By employing Exponential GARCH (EGARCH) model (asymmetric type GARCH model), the study finds evidence that there are significant asymmetric shocks (leverage effect) to volatility in the three series but the intensity of the shocks are not equal for all the series. The results show that the variance structure of high frequencies data is dissimilar from the low frequencies data.

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  • Amir Rafique, 2011. "Comparing the Leverage Effect of Different Frequencies of Stock Returns in an Emerging Market: A Case Study of Pakistan," Information Management and Business Review, AMH International, vol. 3(6), pages 283-288.
    • Handle: RePEc:rnd:arimbr:v:3:y:2011:i:6:p:283-288
    DOI: 10.22610/imbr.v3i6.945
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    References listed on IDEAS

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    1. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    2. Dawood Mamoon & Eatzaz Ahmad, 2008. "Macroeconomic Uncertainty of the 1990s and Volatility at Karachi Stock Exchange," The IUP Journal of Financial Economics, IUP Publications, vol. 0(3), pages 7-28, September.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Ng, Hock Guan & McAleer, Michael, 2004. "Recursive modelling of symmetric and asymmetric volatility in the presence of extreme observations," International Journal of Forecasting, Elsevier, vol. 20(1), pages 115-129.
    5. M. Kabir Hassan & Anisul M. Islam & Syed Abul Basher, 2000. "Market Efficiency, Time-Varying Volatility and Equity Returns in Bangladesh Stock Market," Working Papers 2002_6, York University, Department of Economics, revised Jun 2002.
    6. Schwert, G William, 1990. "Stock Volatility and the Crash of '87," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 77-102.
    7. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    8. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    9. Susan Thomas, 1995. "Heteroscedasticity models on the BSE," Finance 9507007, University Library of Munich, Germany.
    10. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    11. repec:lje:journl:v:2:y:2007:i:2:p:115-149 is not listed on IDEAS
    12. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 9(1), pages 3-21.
    13. John M. Maheu & Thomas McCurdy, 2003. "News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns," CIRANO Working Papers 2003s-38, CIRANO.
    14. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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