Markov Switching Artificial Neural Networks for Modelling and Forecasting Volatility: An Application to Gold Market

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dc.contributor.authorBildirici, Melike
dc.contributor.authorErsin, Özgür
dc.date.accessioned2019-07-22T06:26:39Z
dc.date.available2019-07-22T06:26:39Z
dc.date.issued2015
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThe study analyses the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorporating with neural network architectures. Proposed models differ in terms of both the dynamics of the conditional volatility process and the forecasting capabilities compared to a family of GARCH models. Gray (1996) RS-GARCH model allows regime dependent heteroscedasticity structure following the markov switching methodology of Hamilton (1989). The MS-GARCH-NN model family differ in the sense that, they allow regime switching between GARCH-NN processes. Single regime GARCH-NN models are developed by Donaldson and Kamstra (1996) and further extended by Bildirici and Ersin (2009). Further, the proposed models incorporate a variety of neural network architectures. MS-GARCH-MLP and MS-GARCH-Hybrid-MLP models by Bildirici and Ersin(2014) are augmented with fractional integration (FI) and asymmetric power GARCH variants. And they developed models are MS-FIGARCH-Hybrid-MLP, MS-APGARCH-Hybrid-MLP and MS-FIAPGARCH-Hybrid-MLP models. In this paper, these models were used to test volatility of gold return. Tests are evaluated with MAE, MSE and RMSE criteria and equal forecast accuracy is tested with modified Diebold-Mariano tests. An empirical application is provided for forecasting daily returns in gold market. The results suggest that the proposed approach performs well in modeling and forecasting volatility in daily returns of international gold market.en_US
dc.identifier.doi10.1016/S2212-5671(16)30183-6
dc.identifier.issn2212-5671
dc.identifier.urihttps://doi.org/10.1016/S2212-5671(16)30183-6
dc.identifier.wosWOS:000386630100013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherScienceDirecttr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectHybrid-MLPtr_TR
dc.subjectVolatilitytr_TR
dc.subjectNeural Networkstr_TR
dc.subjectMarkov Switchingtr_TR
dc.subjectMS-FIAPGARCHtr_TR
dc.titleMarkov Switching Artificial Neural Networks for Modelling and Forecasting Volatility: An Application to Gold Marketen_US
dc.typeArticleen_US

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