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Measuring volatility persistence in the presence of sudden changes in the variance of Canadian stock returns

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  • Farooq Malik
  • Bradley Ewing
  • James Payne

Abstract

It is well known that volatility persistence is overestimated if regime shifts are not accounted for in the standard GARCH model. This research detects time periods of sudden changes in variance using the iterated cumulated sums of squares (ICSS) algorithm. Using weekly data for the Canadian stock market indicates that after accounting for endogenously determined volatility shifts in the GARCH model, the estimated persistence in volatility is significantly reduced. This casts some doubt on previous findings that volatility in financial markets is highly persistent. The findings have important implications for investors and financial market participants.

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Bibliographic Info

Article provided by Canadian Economics Association in its journal Canadian Journal of Economics.

Volume (Year): 38 (2005)
Issue (Month): 3 (August)
Pages: 1037-1056

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Handle: RePEc:cje:issued:v:38:y:2005:i:3:p:1037-1056

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Cited by:
  1. Subhani, Muhammad Imtiaz & Hasan, Syed Akif & Osman, Ms. Amber, 2012. "Is Sudden News an Origin of More Systematic Risk in Common Stocks?," MPRA Paper 45139, University Library of Munich, Germany.
  2. Výrost, Tomáš & Baumöhl, Eduard & Lyócsa, Štefan, 2011. "On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries," MPRA Paper 27927, University Library of Munich, Germany.
  3. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 350-360.
  4. Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
  5. Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
  6. Hela Miniaoui & Hameedah Sayani & Anissa Chaibi, 2014. "The Impact of Financial Crisis on Islamic and Conventional Indices of the GCC Countries," Working Papers 2014-401, Department of Research, Ipag Business School.
  7. Andrew stuart Duncan & Guangling"dave" Liu, 2009. "Modelling South African Currency Crises As Structural Changes In The Volatility Of The Rand," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 363-379, 09.
  8. Ritab Al-Khouri & Abdulkhader Abdallah, 2012. "Market liberalization and volatility of returns in emerging markets: The case of Qatar Exchange (QSC)," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing, vol. 5(2), pages 106-115, June.
  9. Kumar, Dilip & Maheswaran, S., 2013. "Detecting sudden changes in volatility estimated from high, low and closing prices," Economic Modelling, Elsevier, vol. 31(C), pages 484-491.
  10. Ahmad Zubaidi Baharumshah & Akram Hasanov & Stilianos Fountas, 2011. "Inflation and inflation uncertainty: Evidence from two Transition Economies," Discussion Paper Series 2011_05, Department of Economics, University of Macedonia, revised Apr 2011.
  11. Efe Çağlar Çağli & Pinar Evrim Mandaci & Pinar Hakan Kahyaoğlu, 2011. "Volatility Shifts and Persistence in Variance: Evidence from the Sector Indices of Istanbul Stock Exchange," International Journal of Economic Sciences and Applied Research (IJESAR), Technological Educational Institute (TEI) of Kavala, Greece, vol. 4(3), pages 119-140, December.
  12. Bidarkota, Prasad V. & Dupoyet, Brice V. & McCulloch, J. Huston, 2009. "Asset pricing with incomplete information and fat tails," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1314-1331, June.
  13. Gordon J. Ross, 2012. "Modeling Financial Volatility in the Presence of Abrupt Changes," Papers 1212.6016, arXiv.org.

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