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

  • 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)

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.

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Article provided by Springer in its journal Spanish Economic Review.

Volume (Year): 1 (1999)
Issue (Month): 3 ()
Pages: 215-238

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Handle: RePEc:spr:specre:v:1:y:1999:i:3:p:215-238
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