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An empirical analysis of alternative parametric ARCH models

Author

Listed:
  • 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)

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

Suggested Citation

  • Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
  • Handle: RePEc:jae:japmet:v:15:y:2000:i:2:p:117-136
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    File URL: http://qed.econ.queensu.ca:80/jae/2000-v15.2/
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    1. 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.
    2. 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.
    3. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    4. Carnero, María Ángeles & Peña, Daniel & Ruiz, Esther, 2001. "Is stochastic volatility more flexible than garch?," DES - Working Papers. Statistics and Econometrics. WS ws010805, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. 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.
    6. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.
    7. M. Pilar Muñoz & M. Dolores Marquez & Lesly M. Acosta, 2007. "Forecasting volatility by means of threshold models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 343-363.
    8. Rodríguez, Mª José & Ruiz, Esther, 2009. "GARCH models with leverage effect : differences and similarities," DES - Working Papers. Statistics and Econometrics. WS ws090302, Universidad Carlos III de Madrid. Departamento de Estadística.
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    11. Thomas Chuffart, 2015. "Selection Criteria in Regime Switching Conditional Volatility Models," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-28, May.
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    15. 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.
    16. 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.
    17. Carnero, María Ángeles & Peña, Daniel & Ruiz, Esther, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. 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.
    19. 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.
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