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Trading Intensity and Intraday Volatility on the Prague Stock Exchange: Evidence from an Autoregressive Conditional Duration Model (in English)

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Author Info

  • Filip Zikes

    ()
    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague)

  • Vít Bubák

    ()
    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague)

Abstract

Using trade and quote data from the Prague Stock Exchange, this study investigates the empirical behavior of price durations defined as the time needed for a quote midpoint to move by a given amount. Focusing on the three most liquid securities traded on the exchange – Cesky Telecom, CEZ, and Komercni banka – the authors estimate autoregressive conditional duration (ACD) models for price-duration series and test several market-microstructure hypotheses suggested by the information-based models of market microstructure. Similar to earlier studies, the authors find that price durations exhibit diurnal patterns, overdispersion, and substantial persistence, which can be adequately captured by the ACD model. The market-microstructure hypotheses, however, find little empirical support in the authors´ results.

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Bibliographic Info

Article provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.

Volume (Year): 56 (2006)
Issue (Month): 5-6 (May)
Pages: 223-245

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Handle: RePEc:fau:fauart:v:56:y:2006:i:5-6:p:223-245

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Related research

Keywords: autoregressive conditional duration; instantaneous volatility; market microstructure;

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References

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  1. Easley, David & O'Hara, Maureen, 1992. " Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
  2. Meitz, Mika & Teräsvirta, Timo, 2004. "Evaluating models of autoregressive conditional duration," Working Paper Series in Economics and Finance 557, Stockholm School of Economics, revised 13 Dec 2004.
  3. Hans R. Stoll, . "The Supply of Dealer Services in Securities Markets," Rodney L. White Center for Financial Research Working Papers 02-78, Wharton School Rodney L. White Center for Financial Research.
  4. Hanousek, Jan & Podpiera, Richard, 2003. "Informed trading and the bid-ask spread: evidence from an emerging market," Journal of Comparative Economics, Elsevier, vol. 31(2), pages 275-296, June.
  5. Song Chen, 2000. "Probability Density Function Estimation Using Gamma Kernels," Annals of the Institute of Statistical Mathematics, Springer, vol. 52(3), pages 471-480, September.
  6. McInish, Thomas H & Wood, Robert A, 1992. " An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks," Journal of Finance, American Finance Association, vol. 47(2), pages 753-64, June.
  7. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," CORE Discussion Papers 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
  9. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1997. "One Day in the Life of a Very Common Stock," Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 805-35.
  10. Easley, David, et al, 1996. " Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-36, September.
  11. Hanousek, Jan & Podpiera, Richard, 2004. "Czech experience with market maker trading system," Economic Systems, Elsevier, vol. 28(2), pages 177-191, June.
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