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Estimating the Dynamics of Weak Efficiency on the Prague Stock Exchange Using the Kalman Filter

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Abstract

The paper builds on the martingale representation of the market efficiency hypothesis and, with the use of an E-GARCH model of the volatility of the PX and PX-GLOBAL daily returns, a state-space model is formulated. Using the Kalman filter, the time-varying dependency of the daily returns on their lagged values is estimated. The estimation of this parameter shows how quickly the Prague Stock Exchange, represented by its PX index and PX-GLOBAL index, has gradually moved toward the condition of weak efficiency.

Suggested Citation

  • Vít Pošta, 2008. "Estimating the Dynamics of Weak Efficiency on the Prague Stock Exchange Using the Kalman Filter," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(05-06), pages 248-260, August.
    • Handle: RePEc:fau:fauart:v:58:y:2008:i:5-6:p:248-260
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Rockinger, Michael & Urga, Giovanni, 2000. "The Evolution of Stock Markets in Transition Economies," Journal of Comparative Economics, Elsevier, vol. 28(3), pages 456-472, September.
    3. Vít Pošta & Zbynìk Hackl, 2007. "Information Efficiency of the Capital Market: a Stochastic Calculus Approach Evidence from the Czech Republic (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 57(5-6), pages 235-254, August.
    4. 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.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    8. 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.
    9. Xiao‐Ming Li, 2003. "China: Further Evidence on the Evolution of Stock Markets in Transition Economies," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(3), pages 341-358, August.
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    Cited by:

    1. Vít Pošta, 2009. "The Role of fundamentals factors of empirical analysis of the Prague stock exchange," Ekonomika a Management, Prague University of Economics and Business, vol. 2009(3).
    2. Walid Abdmoulah, "undated". "Testing the Evolving Efficiency of 11 Arab Stock Markets," API-Working Paper Series 0907, Arab Planning Institute - Kuwait, Information Center.

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    More about this item

    Keywords

    GARCH; Kalman filter; martingale; weak-efficiency;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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