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An empirical analysis of alternative parametric ARCH models

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  • Geoffrey F. Loudon

    (Macquarie University, Australia)

  • Wing H. Watt

    (DBS Bank, Singapore)

  • Pradeep K. Yadav

    (Department of Accounting and Finance, University of Strathclyde, 100 Cathedral Street, Glasgow G4 0LN, UK)

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    Abstract

    This paper presents empirical evidence on the effectiveness of eight different parametric ARCH models in describing daily stock returns. Twenty-seven years of UK daily data on a broad-based value weighted stock index are investigated for the period 1971-97. Several interesting results are documented. Overall, the results strongly demonstrate the utility of parametric ARCH models in describing time-varying volatility in this market. The parameters proxying for asymmetry in models that recognize the asymmetric behaviour of volatility are highly significant in each and every case. However, the 'performance' of the various parameterizations is often fairly similar with the exception of the multiplicative GARCH model that performs qualitatively differently on several dimensions of performance. The outperformance of any model(s) is not consistent across different sub-periods of the sample, suggesting that the optimal choice of a model is period-specific. The outperformance is also not consistent as we change from in-sample inferences to out-of-sample inferences within the same period. Copyright © 2000 John Wiley & Sons, Ltd.Journal: Journal of Applied Econometrics

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

    Volume (Year): 15 (2000)
    Issue (Month): 2 ()
    Pages: 117-136

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    Handle: RePEc:jae:japmet:v:15:y:2000:i:2:p:117-136

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    Cited by:
    1. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    2. Kin-Yip Ho & Ka Cheng Tsui, 2004. "Volatility Dynamics of the Tokyo Stock Exchange: A Sectoral Analysis based on the Multivariate GARCH Approach," Money Macro and Finance (MMF) Research Group Conference 2004 12, Money Macro and Finance Research Group.
    3. Yueh-Neng Lin & Ken Hung, 2008. "Is Volatility Priced?," Annals of Economics and Finance, Society for AEF, vol. 9(1), pages 39-75, May.
    4. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009. "Asymmetric multivariate normal mixture GARCH," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2129-2154, April.
    5. Leeves, Gareth, 2007. "Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia," International Review of Economics & Finance, Elsevier, vol. 16(2), pages 272-286.
    6. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    7. Gabriela De Raaij & Burkhard Raunig, 2005. "Evaluating density forecasts from models of stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 151-166.
    8. Mircea ASANDULUI, 2012. "A Multi-Horizon Comparison Of Volatility Forecasts: An Application To Stock Options Traded At Euronext Exchange Amsterdam," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 10, pages 179-190, December.
    9. Peter Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models:The Model Confidence Set Approach," Working Papers 2003-05, Brown University, Department of Economics.
    10. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2001. "Outliers And Conditional Autoregressive Heteroscedasticity In Time Series," Statistics and Econometrics Working Papers ws010704, Universidad Carlos III, Departamento de Estadística y Econometría.
    11. María José Rodríguez & Esther Ruiz, 2009. "GARCH models with leverage effect : differences and similarities," Statistics and Econometrics Working Papers ws090302, Universidad Carlos III, Departamento de Estadística y Econometría.
    12. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    13. G.R. Pasha & Tahira Qasim & Muhammad Aslam, 2007. "Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 12(2), pages 115-149, Jul-Dec.
    14. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2001. "Is Stochastic Volatility More Flexible Than Garch?," Statistics and Econometrics Working Papers ws010805, Universidad Carlos III, Departamento de Estadística y Econometría.
    15. Twm Evans & David McMillan, 2007. "Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1421-1430.

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