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    Forecasting Volatility İn Oil Prices With A Class Of Nonlinear Volatility Models: Smooth Transition Rbf And Mlp Neural Networks Augmented Garch Approach
    (Springer Link, 2015) Ersin, Özgür; Bildirici, Melike
    In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.
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    An Investigation of Hemophilia, Consanguineous Marriages and Economic Growth: Panel MLP and Panel SVR Approach
    (ScienceDirect, 2015) Bildirici, Melike; Ersin, Özgür; Kökdener, Meltem
    The study has two aims. The first is to investigate the interrelations of haemophilia, consanguineous marriages and their impacts on economic development. The second aim of the paper is to augment the panel regression techniques by incorporating them with Multi-Layer Perceptron neural networks models and Support Vector Machine methods. The extension is proposed to overcome the commonly criticized aspect of panel regressions, the inability to obtain homogeneity in panels. The study utilizes a panel data set that consists of 46 countries covering the 1980-2009 period and models are evaluated in terms of their ability to model the interrelations between the variables analysed. According to the empirical results, the proposed Panel Neural Network Multi-Layer Perceptron and Panel Support Vector Machine models provide success with this purpose. The empirical findings suggest that haemophilia and consanguineous marriages have significant effects on economic development.
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    An Investigation of the Relationship between the Biomass Energy Consumption, Economic Growth and Oil Prices
    (ScienceDirect, 2015) Ersin, Özgür; Bildirici, Melike
    This paper investigates the causality analysis among biomass energy consumption, oil prices and economic growth in Austria, Canada, Germany, Great Britain, Finland, France, Italy, Mexico, Portugal and the U.S. by using the autoregressive distributed lag bounds testing (ARDL) method, Granger causality and Toda and Yamamato non-causality test. The dataset covers the 1970-2013 period. Although many papers have explained the relationship between oil prices and economic growth since 1970, papers have not focused the relationship among biomass energy consumption, oil prices and economic growth. This paper focused the relationship because it was accepted the biomass energy is affected by economic growth and the oil price. For Austria, Germany, Finland and Portugal, the Granger causality test determined the evidence that the conservation hypothesis is supported. In state of U.S., the feedback hypothesis highlights the interdependent relationship between biomass energy consumption and economic growth. Tado Yamamoto test determined, for Austria, Germany, Finland and Portugal, the conservation hypothesis is supported. In state of U.S., the feedback hypothesis highlights the interdependent relationship between biomass energy consumption and economic growth.
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    Markov Switching Artificial Neural Networks for Modelling and Forecasting Volatility: An Application to Gold Market
    (ScienceDirect, 2015) Bildirici, Melike; Ersin, Özgür
    The 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.
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    Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns
    (Hindawi Publishing Corporation, 2014) Ersin, Özgür; Bildirici, Melike
    The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.