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The stationary seasonal hyperbolic asymmetric power ARCH model

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  • Diongue, Abdou Kâ
  • Guégan, Dominique

Abstract

Most financial time series exhibit seasonality, persistence (hyperbolic decay of the autocorrelation function), asymmetric behavior and leptokurticity. The paper introduces the stationary seasonal hyperbolic APARCH model, which can take into account these previous features. Particularly, we examine sufficient and necessary conditions for existence of strict and weak stationary solution. After looking for long memory property of the process, we provide the expression of the likelihoods, in order to estimate the parameters, in three classical cases which appear as particular case of the hyperbolic likelihood.

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  • Diongue, Abdou Kâ & Guégan, Dominique, 2007. "The stationary seasonal hyperbolic asymmetric power ARCH model," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1158-1164, June.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:11:p:1158-1164
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    Cited by:

    1. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    2. Lee, Oesook, 2018. "Stationarity and functional central limit theorem for ARCH(∞) models," Economics Letters, Elsevier, vol. 162(C), pages 107-111.
    3. Conrad, Christian, 2010. "Non-negativity conditions for the hyperbolic GARCH model," Journal of Econometrics, Elsevier, vol. 157(2), pages 441-457, August.
    4. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.

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