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Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility

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  • Feng, Yuanhua
  • McNeil, Alexander J.

Abstract

This paper extends the GARCH model to a wide class of nonstationary processes by proposing a semiparametric GARCH model for simultaneous modelling of conditional heteroskedasticity, slow scale change and periodicity in the volatility of high-frequency financial returns. A data-driven algorithm is developed for estimating the model. An approximate significance test of daily periodicity and the use of Monte Carlo confidence bounds for the scale function are proposed. The practical performance of the proposal is investigated in detail using some German stock price returns. It is shown that the various volatility components are all significant. Asymptotic properties of the proposed estimators are investigated.

Suggested Citation

  • Feng, Yuanhua & McNeil, Alexander J., 2008. "Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility," Economic Modelling, Elsevier, vol. 25(5), pages 850-867, September.
    • Handle: RePEc:eee:ecmode:v:25:y:2008:i:5:p:850-867
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    References listed on IDEAS

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    1. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(3), pages 722-729, June.
    2. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    3. He, Changli & Teräsvirta, Timo, 1999. "FOURTH MOMENT STRUCTURE OF THE GARCH(p,q) PROCESS," Econometric Theory, Cambridge University Press, vol. 15(6), pages 824-846, December.
    4. Liang Peng, 2003. "Least absolute deviations estimation for ARCH and GARCH models," Biometrika, Biometrika Trust, vol. 90(4), pages 967-975, December.
    5. Peng, Liang & Yao, Qiwei, 2003. "Least absolute deviations estimation for ARCH and GARCH models," LSE Research Online Documents on Economics 5828, London School of Economics and Political Science, LSE Library.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    8. Beran, Jan & Feng, Yuanhua, 2002. "SEMIFAR models--a semiparametric approach to modelling trends, long-range dependence and nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 393-419, August.
    9. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Cristina Amado & Annastiina Silvennoinen & Timo Ter¨asvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," NIPE Working Papers 07/2018, NIPE - Universidade do Minho.
    2. repec:pdn:ciepap:104 is not listed on IDEAS
    3. Yuanhua Feng, 2013. "Double-conditional smoothing of high-frequency volatility surface in a spatial multiplicative component GARCH with random effects," Working Papers CIE 65, Paderborn University, CIE Center for International Economics.
    4. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.

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