Bildirici, Melike and Ersin, Özgür (2012): Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models.

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Abstract
Recently, Donaldson and Kamstra (1997) proposed a class of NNGARCH models which are extended to a class of NNGARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTARGARCHNN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FIGARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STARGARCH and LSTARGARCH are extended to their fractionally integrated asymmetric power versions and STARSTFIGARCH and STARSTAPGARCH, STARSTFIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTARLSTGARCHMLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NNGARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTARLSTGARCH models are as follows: LSTARLSTGARCHMLP, LSTARLSTFIGARCHMLP, LSTARLSTAPGARCHMLP and LSTARLSTFIAPGARCHMLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTARLSTGARCH model family is promising and show significant gains in outofsample forecasting. iii. MLPGARCH family provided similar results with the LSTARLSTGARCH family models, except for the MLPFIGARCH and MLPFIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTARLSTGARCHMLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTARLSTAPGARCHMLP model provided the best performance overall.
Item Type:  MPRA Paper 

Original Title:  Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models 
Language:  English 
Keywords:  Volatility, Stock Returns, ARCH, Fractional Integration, MLP, Neural Networks 
Subjects:  F  International Economics > F3  International Finance > F30  General C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  40330 
Depositing User:  Ozgur Ersin 
Date Deposited:  30. Jul 2012 14:30 
Last Modified:  08. Sep 2015 08:35 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/40330 