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Chaoticity Properties of Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes

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Abstract:

Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations.In this work, we have studied the chaoticity properties of FIGARCH (p,d,q) processes by computing mutual information, correlation dimensions, FNNs (False Nearest Neighbour), the largest Lyapunov exponents (LLE) for both the stochastic difference equation and for the financial time series by applying Wolf’s algorithm, Kant’z algorithm and Jacobian algorithm. Although Wolf’s algorithm produced positive LLE’s, Kantz’s algorithm and Jacobian algorithm which are subsequently developed methods due to insufficiency of Wolf’s algorithm generated negative LLE’s constantly.So, as well as experimenting Wolf’s methods’ inefficiency formerly pointed out by Rosenstein (1993) and later Dechert and Gencay (2000), based on Kantz’s and Jacobian algorithm’s negative LLE outcomes, we concluded that it can be suggested that FIGARCH (p,d,q) is not deterministic chaotic process.

Info:

Periodical:
Bulletin of Mathematical Sciences and Applications (Volume 15)
Pages:
69-82
DOI:
https://doi.org/10.18052/www.scipress.com/BMSA.15.69
Citation:
A. Yilmaz and G. Unal, "Chaoticity Properties of Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes", Bulletin of Mathematical Sciences and Applications, Vol. 15, pp. 69-82, 2016
Online since:
May 2016
Authors:
Adil Yilmaz, Gazanfer Unal
Keywords:
Correlation Dimension, Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity, Lyapunov Exponents, Mutual Information
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Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

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