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Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta

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  • Taufiq Choudhry

    (School of Management, University of Southampton, UK)

  • Hao Wu

    (School of Management, University of Southampton, UK)

Abstract

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model: the Kalman method. Forecast errors based on 20 UK company daily stock return (based on estimated time-varying beta) forecasts are employed to evaluate out-of-sample forecasting ability of both GARCH models and Kalman method. Measures of forecast errors overwhelmingly support the Kalman filter approach. Among the GARCH models the GJR model appears to provide somewhat more accurate forecasts than the other bivariate GARCH models. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Taufiq Choudhry & Hao Wu, 2008. "Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 670-689.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:8:p:670-689
    DOI: 10.1002/for.1096
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    1. Robert D. Brooks & Robert W. Faff & Michael D. McKenzie, 1998. "Time†Varying Beta Risk of Australian Industry Portfolios: A Comparison of Modelling Techniques," Australian Journal of Management, Australian School of Business, vol. 23(1), pages 1-22, June.
    2. Mahmoud Wahab, 1995. "Conditional dynamics and optimal spreading in the precious metals futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(2), pages 131-166, April.
    3. Meade, Nigel, 2002. "A comparison of the accuracy of short term foreign exchange forecasting methods," International Journal of Forecasting, Elsevier, vol. 18(1), pages 67-83.
    4. Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 399-421, August.
    5. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    6. R.W. Faff & R.D. Brooks, 1998. "Time-varying Beta Risk for Australian Industry Portfolios: An Exploratory Analysis," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5&6), pages 721-745.
    7. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Lee, Tae-Hwy, 1994. "Spread and volatility in spot and forward exchange rates," Journal of International Money and Finance, Elsevier, vol. 13(3), pages 375-383, June.
    10. Schwert, G William & Seguin, Paul J, 1990. " Heteroskedasticity in Stock Returns," Journal of Finance, American Finance Association, vol. 45(4), pages 1129-1155, September.
    11. Fabozzi, Frank J. & Francis, Jack Clark, 1978. "Beta as a Random Coefficient," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 13(01), pages 101-116, March.
    12. Fornari, Fabio & Mele, Antonio, 1996. "Modeling the changing asymmetry of conditional variances," Economics Letters, Elsevier, vol. 50(2), pages 197-203, February.
    13. Black, A. & Fraser, P. & Power, D., 1992. "UK unit trust performance 1980-1989: A passive time-varying approach," Journal of Banking & Finance, Elsevier, vol. 16(5), pages 1015-1033, September.
    14. Koutmos, Gregory & Lee, Unro & Theodossiu, Panayiotis, 1994. "Time-varying betas and volatility persistence in International Stock markets," Journal of Economics and Business, Elsevier, vol. 46(2), pages 101-112, May.
    15. Engel, Charles & Rodrigues, Anthony P, 1989. "Tests of International CAPM with Time-Varying Covariances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(2), pages 119-138, April-Jun.
    16. K. Giannopoulos, 1995. "Estimating the time Varying Components of international stock markets' risk," The European Journal of Finance, Taylor & Francis Journals, vol. 1(2), pages 129-164.
    17. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    18. 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.
    19. Klemkosky, Robert C & Martin, John D, 1975. "The Adjustment of Beta Forecasts," Journal of Finance, American Finance Association, vol. 30(4), pages 1123-1128, September.
    20. 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.
    21. Jun Yu, 2002. "Forecasting volatility in the New Zealand stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 12(3), pages 193-202.
    22. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    23. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    24. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    25. Braun, Phillip A & Nelson, Daniel B & Sunier, Alain M, 1995. " Good News, Bad News, Volatility, and Betas," Journal of Finance, American Finance Association, vol. 50(5), pages 1575-1603, December.
    26. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    27. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    28. Bodurtha, James N, Jr & Mark, Nelson C, 1991. " Testing the CAPM with Time-Varying Risks and Returns," Journal of Finance, American Finance Association, vol. 46(4), pages 1485-1505, September.
    29. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    30. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    31. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    32. 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.
    33. Ng, Lilian, 1991. " Tests of the CAPM with Time-Varying Covariances: A Multivariate GARCH Approach," Journal of Finance, American Finance Association, vol. 46(4), pages 1507-1521, September.
    34. Bos, T & Newbold, P, 1984. "An Empirical Investigation of the Possibility of Stochastic Systematic Risk in the Market Model," The Journal of Business, University of Chicago Press, vol. 57(1), pages 35-41, January.
    35. Robert W. Faff & David Hillier & Joseph Hillier, 2000. "Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 27(5&6), pages 523-554.
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    2. Асатуров К.Г., 2015. "Динамические Модели Систематического Риска: Сравнение На Примере Индийского Фондового Рынка," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 51(4), pages 59-75, октябрь.
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    5. Suthawan Prukumpai, 2015. "Time-varying Industrial Portfolio Betas under the Regime-switching Model: Evidence from the Stock Exchange of Thailand," Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 22(2), pages 54-76, December.
    6. Entrop, O. & von la Hausse, L. & Wilkens, M., 2017. "Looking beyond banks’ average interest rate risk: Determinants of high exposures," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 204-218.
    7. Xu, Weijun & Liu, Guifang & Li, Hongyi, 2016. "A novel jump diffusion model based on SGT distribution and its applications," Economic Modelling, Elsevier, vol. 59(C), pages 74-92.
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    11. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.

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