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Modelling and forecasting the volatility of the portuguese stock index PSI-20

  • Caiado, Jorge

The volatility clustering often seen in financial data has increased the interest of researchers in applying good models to measure and forecast stock returns. This paper aims to model the volatility for daily and weekly returns of the Portuguese Stock Index PSI-20. By using simple GARCH, GARCH-M, Exponential GARCH (EGARCH) and Threshold ARCH (TARCH) models, we find support that there are significant asymmetric shocks to volatility in the daily stock returns, but not in the weekly stock returns. We also find that some weekly returns time series properties are substantially different from properties of daily returns, and the persistence in conditional volatility is different for some of the sub-periods referred. Finally, we compare the forecasting performance of the various volatility models in the sample periods before and after the terrorist attack on September 11, 2001.

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File URL: https://mpra.ub.uni-muenchen.de/2304/1/MPRA_paper_2304.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2077.

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Date of creation: 2004
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Publication status: Published in Portuguese Journal of Management Studies NÂș1.XI(2004): pp. 3-21
Handle: RePEc:pra:mprapa:2077
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  1. Schwert, G William, 1990. "Stock Volatility and the Crash of '87," Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 77-102.
  2. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-44.
  3. Paul H. Kupiec, 1989. "Initial margin requirements and stock returns volatility: another look," Finance and Economics Discussion Series 53, Board of Governors of the Federal Reserve System (U.S.).
  4. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  5. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
  6. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  7. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
  8. Ratner, Mitchell, 1996. "Investigating the behavior and characteristics of the Madrid Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 20(1), pages 135-149, January.
  9. Sentana,E., 1995. "Quadratic Arch Models," Papers 9517, Centro de Estudios Monetarios Y Financieros-.
  10. Chou, Ray Yeutien, 1988. "Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 279-94, October-D.
  11. Bevan Blair & Ser-Huang Poon & Stephen Taylor, 2002. "Asymmetric and crash effects in stock volatility for the S&P 100 index and its constituents," Applied Financial Economics, Taylor & Francis Journals, vol. 12(5), pages 319-329.
  12. Attanasio, Orazio P, 1991. "Risk, Time-Varying Second Moments and Market Efficiency," Review of Economic Studies, Wiley Blackwell, vol. 58(3), pages 479-94, May.
  13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  14. 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.
  15. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
  16. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
  17. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  18. Gregorios Siourounis, 2002. "Modelling volatility and testing for efficiency in emerging capital markets: the case of the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 12(1), pages 47-55.
  19. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
  20. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  21. E. Dockery & F. Vergari, 1997. "Testing the random walk hypothesis: evidence for the Budapest stock exchange," Applied Economics Letters, Taylor & Francis Journals, vol. 4(10), pages 627-629.
  22. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September.
  23. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
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