Modeling the Realized Volatility with ARFIMA and Unobserved Component Models: Results from the Polish Financial Market

• Autorzy: Małgorzata Doman
• Języki publikacji: EN

Abstrakty

EN

Chapter 7 is focused on the notion of realized volatility in financial econometrics. The chapter presents an approach to the estimation of the daily realized volatility based on intraday returns. It also takes into account effects of market microstructure. The volatility has been modeled and predicted for stock index WIG20 and exchange rate USD/PLN using ARFIMA and unobserved component models. The findings are that modeling realized volatility with UC and ARFIMA models provides comparable volatility forecasts. (fragment of text)

Słowa kluczowe

EN

Stochastic Volatility Processes; Autoregressive fractionally integrated moving average model (ARFIMA); Econometrics; Financial markets

PL

Procesy zmienności stochastycznej; Model ARFIMA; Ekonometria; Rynki finansowe

• Czasopismo: FindEcon Monograph Series : advances in financial market analysis
• Rocznik: 2006
• Numer: nr 2 Financial markets : principles of modeling forecasting and decision-making
• Strony: 121--135

Twórcy

autor

Małgorzata Doman

• Poznań University of Economics, Poland

Bibliografia

• Andersen T. G., Bollerslev T. (1998), "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts", International Economic Review, 39, 885-905.
• Andersen T. G., Bollerslev T. (1997), "Intraday Periodicity and Volatility Persistence in Financial Markets", Journal of Empirical Finance, 4, 115-158.
• Andersen T. G., Bollerslev T., Diebold F. X., Labys P. (2001), "The Distribution of Realized Exchange Rate Volatility", Journal of the American Statistical Association, 96, 42-55.
• Bandorff-Nielsen O. E., Shephard N. (2002), "Econometric Analysis of Realised Volatility and Its Use in Estimating Stochastic Valatility Models", Journal of the Royal statistical Society, 64, Series B, 253-280.
• Davidson J. (2003), Time Series Modeling Version 3.23, Cardiff: Cardiff University.
• Durbin J., Koopman S. J. (2002), Time Series Analysis by State Space Methods, Oxford: Oxford University Press.
• Geweke J., Porter-Hudak S. (1983), "The Estimation and Application of Long Memory Time Series Models", Journal of Time Series Analysis, 4(4), 221-238.
• Granger C. W. J., Joyeux R. (1980), "An Introduction to Long-Memory Time Series Models and Fractional integration", Journal of Time Series Analysis, 1(1), 15-29.
• Hosking J. R. M. (1981), "Fractional Differencing", Biometrica, 68, 165-176.
• Koopman S. J., Hoi E. (2002), "Stock Index Volatility Forecasting with High Frequency Data", Tinbergen Institute Discussion Paper, 068/4.
• Lo A. W. (1991), "Long-Term Memory in Stock Market Prices", Econometrica, 59, 1279-1313.
• Martens M. (2002), "Measuring and Forecasting S&P 500 Index-Futures Volatility Using High-Frequency Data", Journal of Futures Markets, 22, 497-518.
• Robinson P. M. (1994), "Semiparametric Analysis of Long-Memory Time Series", Annals of Statistics, 22, 515-539.
• Tsay R. S. (2002), Analysis of Financial Time Series, Wiley Series in Probability and Statistics, New York: Wiley& Sons.