2009 | 10 | 1 | 67-75
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A large package of information is being reflected in stock prices during a short period after opening. Moreover, the start-of-the-day (morning) volatility has a strong impact on the price variability during all the day. In this connection, the question is whether the morning realized variance calculated as the sum of morning squared intraday returns can be useful in forecasting the daily realized variance (end-of-the-day volatility). In the paper, we apply three different methods of forecasting the daily realized variance for stocks quoted on the Warsaw Stock Exchange Our findings show that the morning realized variance provides valuable information that can be used in forecasting the daily realized variance.
  • Katedra Matematyki Stosowanej, UE w Poznaniu
  • Wydział Matematyki i Informatyki, AMU w Poznaniu
  • Andersen T.G., Bollerslev T. (1998), Answering the Skeptics: Yes, Standard Vola-tility Models Do Provide Accurate Forecasts, International Economic Review 39, 885- 905.
  • Andersen T.G., Bollerslev T., Diebold F.X., Ebens H. (2001) The Distribution of Realized Stock Return Volatility, Journal of Financial Economics, 61, 43-76.
  • Barndorff-Nielsen O.E., Shephard N. (2002) Econometric analysis of realized vola-tility and its use in estimating stochastic volatility models, Journal of the Royal Statistical Society, Series B, 63, 253-280.
  • Doman M. (2006), Modeling the Realized Volatility with ARFIMA and Unob-served Component Models: Results from the Polish Financial markets, in: Milo W., Wdowiński P. (eds), Financial Markets. Principal of Modeling, Forecasting and Decision-Making, Lodz University Press, Lodz, 123-137.
  • Doman M., Doman R. (2004) Ekonometryczne modelowanie dynamiki polskiego rynku finansowego, Wydawnictwo Akademii Ekonomicznej w Poznaniu, Poznań
  • Durbin J., Koopman S.J. (2002), Time Series Analysis by State Space Methods, Oxford University Press, Oxford.
  • Frijns B., Margaritis D. (2008), Forecasting daily volatility with intraday data, The European Journal of Finance 14, 523-540.
  • Granger C. W. J. and R. Joyeux (1980) An Introduction to Long-Memory Time Series Models and Fractional integration, Journal of Time Series Analysis 1, 15-29
  • Hosking J. R. M. (1981) Fractional Differencing, Biometrica 68, 165-176.
  • Koopman S.J., Jungbacker B., Hol E. (2005) Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurement, Journal of Empirical Finance 12, 445-475.
  • Poon S.-H. (2005) A Practical Guide to Forecasting Financial Market Volatility, John Wiley, Chichester.
  • Tsay R.S. (2002), Analysis of Financial Time Series, John Wiley, New York.
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