Higher-order dependence in the general Power ARCH process and a special case
AbstractIn this paper we consider a general first-order power ARCH process and, in particular, a special case in which the power parameter approaches zero. These considerations give us the autocorrelation function of the logarithms of the squared observations for first-order exponential and logarithmic GARCH processes. These autocorrelations decay exponentially with the lag and may be used for checking how well an estimated exponential or logarithmic GARCH model characterizes the corresponding autocorrelation structure of the observations. The results of the paper are also useful in illustrating differences in the autocorrelation structures of the classical first-order GARCH and the exponential or logarithmic GARCH models.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 315.
Length: 16 pages
Date of creation: 21 Apr 1999
Date of revision:
Publication status: Published in Recent advances in linear models and related areas, Shalabh, X, Heumann, C. (eds.), 2008, pages 231-251, Springer.
Note: The forthcoming version of the paper is C. He, H. Malmsten and T. Teräsvirta: Higher-order dependence in the general Power ARCH process and the role of the power parameter
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Box-Cox transformation; conditional heteroskedasticity; exponential GARCH; logarithmic GARCH; higher-order dependence;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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