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Estimating the time Varying Components of international stock markets' risk


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  • K. Giannopoulos
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    In this study an alternative approach for assessing securities' risk is applied. Various authors have argued that security returns are not homoskedastic but exhibit variation over time. They have observed that large changes tend be followed by more large changes in either direction, and so volatility must be predictably high after large changes. This phenomenon of securities' volatility, referred to as clustering, has important implications for security pricing and risk management. Among the most popular techniques currently used to capture the clustering effect and to forecast future volatilityare those belonging to the family of Autoregressive Conditional Heteroskedastic (ARCH) models. The main aim of this paper is to investigate whether such volatility modelling can be used to capture the time variation not only in the total risk of a security return but also its systematic and unsystematic components. Using weekly local stock market data, the time varying beta with the World Index has been estimated via a bivariate GARCH-M model. The GARCH-M parameterization used here is a dynamic specification of the SIM. The hypothesis that this dynamic specification holds cannot be rejected for 11 out of 13 local portfolios. The results provide evidence that both the systematic and the non-systematic counterparts are also changing over time. However, in some markets those risk changes may take place with some delay. This suggests that some of the low correlation coefficients computed for certain stock market returns may not be due to differences in business cycles among those countries, but may be caused by the non-synchronous response to world market developments. This finding should have important implications in many investment decisions such as portfolio selection, market timing and risk hedging.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 1 (1995)
    Issue (Month): 2 ()
    Pages: 129-164

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    Handle: RePEc:taf:eurjfi:v:1:y:1995:i:2:p:129-164

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    Keywords: ARCH; SIM; time varying betas;


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    Cited by:
    1. Coleman, Jane A. & Shaik, Saleem, 2009. "Time-Varying Estimation of Crop Insurance Program in Altering North Dakota Farm Economic Structure," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49516, Agricultural and Applied Economics Association.
    2. Andrew Worthington & Helen Higgs, 2005. "Market Risk in Demutualised Self-Listed Stock Exchanges: An International Analysis of Selected Time-Varying Betas," School of Economics and Finance Discussion Papers and Working Papers Series 201, School of Economics and Finance, Queensland University of Technology.
    3. R. D. Brooks & R. W. Faff & M. McKenzie, 2002. "Time varying country risk: an assessment of alternative modelling techniques," The European Journal of Finance, Taylor & Francis Journals, vol. 8(3), pages 249-274.
    4. C. J. Adcock & E. A. Clark, 1999. "Beta lives - some statistical perspectives on the capital asset pricing model," The European Journal of Finance, Taylor & Francis Journals, vol. 5(3), pages 213-224.
    5. Mattia Ciprian & Stefano d'Addona, 2005. "Time Varying Sensitivities on a GRID architecture," Finance 0511007, EconWPA.
    6. Choudhry, Taufiq, 2004. "The hedging effectiveness of constant and time-varying hedge ratios using three Pacific Basin stock futures," International Review of Economics & Finance, Elsevier, vol. 13(4), pages 371-385.
    7. Chevapatrakul, Thanaset, 2013. "Return sign forecasts based on conditional risk: Evidence from the UK stock market index," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2342-2353.
    8. Gangemi, Michael & Brooks, Robert & Faff, Robert, 1999. "Mean reversion and the forecasting of country betas: a note," Global Finance Journal, Elsevier, vol. 10(2), pages 231-245.
    9. Sascha Mergner & Jan Bulla, 2008. "Time-varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques," The European Journal of Finance, Taylor & Francis Journals, vol. 14(8), pages 771-802.


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