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Modelling conditional heteroskedasticity: Application to the "IBEX-35" stock-return index

Author

Listed:
  • Angel León

    (Departamento de Economía Financiera, Universidad de Alicante, Apartado de Correos 99, E-03080 Alicante, Spain Departamento de Fundamentos del Análisis Económico, Universidad de Alicante, Apartado de Correos 99, E-03080 Alicante, Spain)

  • Juan Mora

    (Departamento de Economía Financiera, Universidad de Alicante, Apartado de Correos 99, E-03080 Alicante, Spain Departamento de Fundamentos del Análisis Económico, Universidad de Alicante, Apartado de Correos 99, E-03080 Alicante, Spain)

Abstract

This paper compares alternative time-varying volatility models for daily stock-returns using data from Spanish equity index IBEX-35. Specifically, we estimate a parametric family of models of generalized autoregressive heteroskedasticity (which nests the most popular symmetric and asymmetric GARCH models), a semiparametric GARCH model, the generalized quadratic ARCH model, the stochastic volatility model, the Poisson Jump Diffusion model and, finally, a nonparametric model. Those models which use conditional standard deviation (specifically, TGARCH and AGARCH models) produce better fits than all other GARCH models. We also compare the within sample predictive power of all models using a standard efficiency test. Our results show that the asymmetric behaviour of responses is a statistically significant characteristic of these data. Moreover, we observe that specifications with a distribution which allows for fatter tails than a normal distribution do not necessarily outperform specifications with a normal distribution.

Suggested Citation

  • Angel León & Juan Mora, 1999. "Modelling conditional heteroskedasticity: Application to the "IBEX-35" stock-return index," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(3), pages 215-238.
    • Handle: RePEc:spr:specre:v:1:y:1999:i:3:p:215-238
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    Citations

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    Cited by:

    1. Trino-Manuel Niguez & Javier Perote, 2004. "Forecasting the density of asset returns," STICERD - Econometrics Paper Series 479, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Brooks, Robert, 2007. "Power arch modelling of the volatility of emerging equity markets," Emerging Markets Review, Elsevier, vol. 8(2), pages 124-133, May.
    3. Gabriele Fiorentini & Angel León & Gonzalo Rubio, "undated". "Short-term options with stochastic volatility: Estimation and empirical performance," Studies on the Spanish Economy 02, FEDEA.
    4. Vicente Meneu & Hipòlit Torró, 2003. "Asymmetric covariance in spot‐futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(11), pages 1019-1046, November.
    5. Cunado Eizaguirre, Juncal & Biscarri, Javier Gomez & Hidalgo, Fernando Perez de Gracia, 2004. "Structural changes in volatility and stock market development: Evidence for Spain," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1745-1773, July.
    6. Fiorentini, Gabriele & Leon, Angel & Rubio, Gonzalo, 2002. "Estimation and empirical performance of Heston's stochastic volatility model: the case of a thinly traded market," Journal of Empirical Finance, Elsevier, vol. 9(2), pages 225-255, March.
    7. Rubio Irigoyen, Gonzalo & Ferreira García, María Eva & Gago, Mónica & León, Angel, 2002. "An empirical comparison of the performance of alternative option pricing models," DFAEII Working Papers 1988-088X, University of the Basque Country - Department of Foundations of Economic Analysis II.
    8. Eva Ferreira & Mónica Gago & Angel León & Gonzalo Rubio, 2005. "An empirical comparison of the performance of alternative option pricing models," Investigaciones Economicas, Fundación SEPI, vol. 29(3), pages 483-523, September.
    9. Carnero, María Ángeles, 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.
    10. Trino-Manuel Ñíguez, 2008. "Volatility and VaR forecasting in the Madrid Stock Exchange," Spanish Economic Review, Springer;Spanish Economic Association, vol. 10(3), pages 169-196, September.
    11. Pena, Ignacio & Rubio, Gonzalo & Serna, Gregorio, 1999. "Why do we smile? On the determinants of the implied volatility function," Journal of Banking & Finance, Elsevier, vol. 23(8), pages 1151-1179, August.
    12. Juan Luis Nicolau, 2001. "Parametric And Nonparametric Approaches To Event Studies: An Application To A Hotel'S Market Value," Working Papers. Serie AD 2001-08, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. Pilar Corredor Casado & Rafael Santamaría, "undated". "La estructura temporal de las volatilidades implícitas en la opción sobre el Ibex-35," Studies on the Spanish Economy 04, FEDEA.
    14. Trino-Manuel Ñíguez, 2003. "Volatility And Var Forecasting For The Ibex-35 Stock-Return Index Using Figarch-Type Processes And Different Evaluation Criteria," Working Papers. Serie AD 2003-33, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    15. Esther B. Del Brio & Javier Perote & Julio Pindado, 2003. "Measuring the Impact of Corporate Investment Announcements on Share Prices: The Spanish Experience," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(5‐6), pages 715-747, June.

    More about this item

    Keywords

    Conditional heteroskedasticity; prediction; stock-return index;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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