DSpace Arşivi :: by Yazar "Bildirici, Melike" değerine göre listeleniyor

Yazar "Bildirici, Melike" seçeneğine göre listele

Listeleniyor 1 - 18 / 18

ASYMMETRIC POWER AND FRACTIONALLY INTEGRATED SUPPORT VECTOR AND NEURAL NETWORK GARCH MODELS WITH AN APPLICATION TO FORECASTING FINANCIAL RETURNS IN ISE100 STOCK INDEX
(Editura Ase, 2014) Bildirici, Melike; Ersin, Ozgur Omer
The study aims to augment commonly applied volatility models with support vector machines and neural networks. Further, fractional integration and asymmetric powers will be introduced. The proposed modeling strategy benefits from neural network based GARCH models and SVR-GARCH models. Following these approaches, the study proposed fractional integration and asymmetric power GARCH structures to obtain SVR-FIAPGARCH and NN-FIAPGARCH models to be evaluated in terms of learning algorithms. Models are evaluated for in-sample and out-of-sample forecasting of daily returns in Istanbul ISE100 stock index. Results suggest several findings: i. fractional integration and asymmetric power structures could be modeled with learning algorithms. ii. volatility clustering, asymmetry and nonlinearity characteristics are modeled more effectively with SVR-GARCH and MLP-GARCH models compared to the GARCH models. iii. SVR-GARCH models provided the lowest error criteria levels in out-of-sample and are closely followed by the MLP-GARCH models.
ASYMMETRY IN THE ENVIRONMENTAL POLLUTION, ECONOMIC DEVELOPMENT AND PETROL PRICE RELATIONSHIP: MRS-VAR AND NONLINEAR CAUSALITY ANALYSES
(Inst Economic Forecasting, 2019) Ersin, Ozgur; Bildirici, Melike
The paper aims at assessing the nonlinear relations among the carbon dioxide (CO2) emissions, petrol prices and the level of economic prosperity in USA and UK for the 1861-2012 and 1871-2012 periods. By covering one of the largest samples, the paper achieves its novelty through the utilization of the Markov-switching based VAR and Granger causality methodologies to provide important insights regarding the shape of the Environmental Kuznets Curve. The selection of UK and USA is not only based on the availability of the largest data, but also on selecting countries that have gone through the stages of economic development in the sense of Rostow. In addition, the impact of the petrol prices is introduced to the environment-income relation. With this respect, the paper achieves a link between the literature of the regime-switching based business cycles and the environmental economics. The empirical findings of the paper show that: (i) the asymmetric impacts of the economic growth rates on emissions cannot be rejected both for the expansionary and for the recessionary periods in both countries in addition to the nonlinear effects of petrol prices, (ii) the nonlinear Granger causality results reveal bidirectional effects in both regimes which hold especially for the USA compared to the UK, (iii) the efficiency of the MRS-VAR models in capturing historical recession dates is confirmed since they coincide with major success to those reported by NBER and ECRI, (iv) the difference in persistence and durations of regimes should be taken into consideration. Within a policy perspective, the practical implications of the paper suggest further improvement in policies directed towards the environmental impact of economic growth in addition to petrol prices in different phases of the business cycles. The MRS-VAR findings also reveal practical implications regarding the shape of EKC: considering the size and the significance of the parameters in different regimes, the inverted U shape does not hold for the USA since the positive effects on emissions dominate in both regimes. In the UK, the negative effect is dominant only in regime 2. The paper also discusses the need of caution due to the shift of production from the developed to the far east - creating a derived demand of world's environmental degradation.
Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA
(Springer Heidelberg, 2018) Bildirici, Melike; Ersin, Ozgur Omer
The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.
Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA (vol 25, pg 200, 2017)
(Springer Heidelberg, 2018) Bildirici, Melike; Ersin, Ozgur Omer
The original publication of this paper contains a mistake.
An Empirical Analysis of the Effects of Consanguineous Marriages on Economic Development
(Sage Publications Inc, 2010) Bildirici, Melike; Kokdener, Meltem; Ersin, Ozgur Omer
In this study, development experiences toward economic development are investigated to provide an alternative analysis of economic development, human capital, and genetic inheritance in the light of consanguineous marriages. The countries analyzed in the study are discussed in accordance with consanguineous marriage practices and classified by their per capita gross domestic product (GDP) growth. A broad range of countries are analyzed in the study. Arab countries that experienced high rates of growth in their gross national income during the twentieth century but failed to fulfill adequate development measures as reflected in the growth in national income, countries undergoing transition from tight government regulation to free market democracy, and African nations that have experienced complications in the process of development show important differences in the process of economic development. It is shown that the countries that have reached high average development within the context of per capita GDP have overcome problems integral to consanguineous marriage.
EXAMINATION OF THE PREDICTABILITY OF BDI AND VIX : A THRESHOLD APPROACH
(Fabrizio Serra Editore, 2019) Bildirici, Melike; Ersin, Ozgur; Onat, Isil Sahin
The market volatility index (VIX) and the Baltic dry index (BDI) are evaluated as two important economic indicators, the former as being a gauge of investor's fear and risk and the latter as being a reflection of costs associated to shipment of dry cargo. However, the empirical analyses aiming at achieving future forecasts suffer drastically due to inherent threshold effects the leptokurtic distribution The purpose of the paper is to propose the application of threshold models that allow regime dependent dynamics in the conditional mean and variance processes simultaneously to overcome this difficulty. Accordingly, four different threshold GARCH specifications are evaluated: TAR-GARCH, TAR-TGARCH, TAR-TR-GARCH and TAR-TR-TGARCH which allow more complex threshold behavior as one moves from the former to the letter. The empirical findings show the following, i. all of the four models provide significant improvements in terms of out-of-sample forecasting, ii. the threshold effects are dominant in both series and the proposed threshold models are capable to overcome the ARCH effects, iii. without the simultaneous modelling of threshold effects in the mean and variance processes, additional within regime specifications are needed to account for the negative and positive innovations, iv. BDI and VIX are two indexes that should be modeled with caution and once controlled for the threshold effects, they possess significant potential to be taken as future leading economic indicators.
Forecasting oil prices: Smooth transition and neural network augmented GARCH family models
(Elsevier Science Bv, 2013) Bildirici, Melike; Ersin, Ozgur Omer
The study focuses on a new class of nonlinear volatility models based on neural networks and STAR type nonlinearity. Accordingly, LSTAR-LST-GARCH family and LSTAR-LST-GARCH-NN family of models will be evaluated to analyze petrol prices with economic applications. The nonlinear behavior and leptokurtic distribution are discussed in many studies. The study aims proposing augmentation of linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models with LSTAR type nonlinearity modeling. Further, the proposed models will be augmented with neural networks to benefit from well known learning and forecasting capabilities. The multilayer perceptron (MLP) neural network model and LSTAR model have significant similarities in terms of their architecture. The proposed LSTAR-LST-GARCH family and ANN augmented LSTAR-LST-GARCH-MLP models are evaluated for modeling petrol prices. Empirical findings of the study are: (1) Fractionally integrated and asymmetric power improvements among the GARCH family models provide better forecasting capability for petrol prices; better captured long memory and high volatility characteristics of petrol prices. (2). LSTAR-LST-GARCH model family results in even better gains in out-of-sample forecasting. (3) Donaldson and Kamstra (1997) based MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models. One exception is for MLP-FIGARCH and MLP-FIAPGARCH models; FI and AP augmented models proposed in this study. (4) Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are best captured with the LSTAR-LST-GARCH-MLP model family. Forecasting capabilities of neural network techniques are promising. Among the evaluated models, the LSTAR-LST-APGARCH-MLP model provided the best performance overall. With a political perspective, in addition to the highly volatile structure, the long memory characteristics of petrol prices requires that the economic policy interventions should be kept at the modest levels to avoid persistent impacts of shocks. (C) 2013 Elsevier B.V. All rights reserved.
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.
Genetic structure, consanguineous marriages and economic development: Panel cointegration and panel cointegration neural network analyses
(Pergamon-Elsevier Science Ltd, 2011) Bildirici, Melike; Ersin, Ozgur Omer; Kokdener, Meltem
Consanguineous marriages and their effects on human beings in light of biological effects of genetic sicknesses are discussed in many studies. Among many, the likelihood of sicknesses such as phenylketonuria, thalassemia, Landsteiner-Fanconi-Anderson's syndrome, hemophilia and many neuro system anomalies increase drastically in countries with consanguineous marriage practices resulting in increasing economic costs. In the study, we aimed to analyze the effects of consanguineous marriage and its effect on economic growth and development. We also analyzed infant mortality in these countries in light of consanguineous marriages and economic development. In the study, Panel Cointegration specifications are integrated into Neural Network models known with their strong generalization properties. The study focuses the econometric analyses, where the Panel Cointegration Neural Network Model is investigated and compared to the Panel Cointegration Model. According to MSE, MAE and RMSE error criteria and Die-bold Mariano tests of equal forecast accuracy, the results suggest strong advantages of Panel Cointegration MLP models compared to Panel Cointegration models used in regression analysis. (C) 2010 Elsevier Ltd. All rights reserved.
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.
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.
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.
Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices
(Springer Heidelberg, 2018) Bildirici, Melike; Ersin, Ozgur
The paper aims at evaluating the nonlinear and complex relations between CO2 emissions, economic development, and petrol prices to obtain new insights regarding the shape of the environmental Kuznets curve (EKC) in the USA and in the UK in addition to introducing a newly proposed nonlinear approach. Within this respect, the paper has three purposes: the first one is to combine the multilayer perceptron neural networks (MLP) with Markov-switching vector autoregressive (MS-VAR) type nonlinear models to obtain the MS-VAR-MLP model. The second is to utilize one of the largest datasets in the literature covering the 1871-2016 period, a long span of data starting from the late eighteenth century. Since the emission, economic development, and petrol price relation is subject to nonlinearity and trajectory changes due to many historical events, the development of the MS-VAR-MLP model is a necessity to contribute to the ongoing debate regarding the shape of the EKC curve and the stability of the relation. The third purpose is to develop the MS-VAR-MLP-based regime-dependent sensitivity analysis, which eases the visual interpretation of the nonlinear causal relationships, which are allowed to have asymmetric interactions in different phases of the expansionary and recessionary periods of the business cycles. Our results provide clear deviations from the findings in the literature: (i) the shape of the EKC curve cannot be assumed to be stable and is subject to regime dependency, nonlinearity, and magnitude dependency; (ii) the forecast results suggest that incorporation of regime switching and neural networks provide significant improvement over the MS-VAR counterpart; and (iii) for both USA and UK and for the 1871-2016 period, the positive impacts of economic growth on emissions cannot be rejected for the majority of the phases of the business cycles; however, the magnitude of this effect is at various degrees. In addition, the incorporation of petrol price provides significant findings considering its effects on emission and economic growth rates. The analysis suggest clear deviations from the expected shape of the EKC curve and puts forth the necessity to utilize more complex empirical methodologies to evaluate the EKC since the emissions-economic development relation is more complex than it was assumed. Following these findings, several policy recommendations are provided. Lastly, the proposed MS-VAR-MLP methodology is compared with the MS-VAR model and various advantages and disadvantages are enumerated.
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.
A NONLINEAR ANALYSIS OF MONETARY POLICY WITH DOMINANCE INDICES IN TURKEY: MS-VAR APPROACH
(Inst Economic Forecasting, 2017) Ersin, Ozgur; Bildirici, Melike
The study focuses on analyzing the policies followed in Turkey based on inflation targeting with an application of interest rate corridor policy in which the spread between the two policy rates, namely, the borrowing and lending rates. To overcome the difficulty of two different policy rates, two indices, the PDI and the MDI are utilized to capture the response of the monetary authority within a nonlinear Taylor rule context. The empirical findings for the Turkish economy with MS-VAR and MS-Granger causality analyses suggest that while the policy interest rates are determined in the spirit of the Taylor rule, the monetary policy involuntarily affirms inflation after the application of the policy, a finding that is consistent with the FTPL theory. As a result, as the central bank tries to establish price stability and financial stability with two policy interest rates, accepting higher inflation rates could be unavoidable. The results also are in favor of bi-directional nonlinear causality which led to important policy implications.
NONLINEARITY, VOLATILITY AND FRACTIONAL INTEGRATION IN DAILY OIL PRICES: SMOOTH TRANSITION AUTOREGRESSIVE ST-FI(AP)GARCH MODELS
(Inst Economic Forecasting, 2014) Bildirici, Melike; Ersin, Ozgur Omer
The study aims to extend the GARCH type volatility models to their nonlinear TAR (Tong, 1990) and STAR-based (Terasvirta, 1994) counter parts where both the conditional mean and the conditional variance processes follow TAR and STAR nonlinearity. The paper further investigates the models under their fractional integration and asymmetric power variants. The STAR-based models are LSTAR-LST-GARCH, LSTAR-LST-FIGARCH, LSTAR-LST-FIPGARCH and LSTAR-LST-FIAPGARCH models, which may be easily applied to model and forecast various financial time series. In the empirical section, an application is provided to model the daily returns in WTI crude oil prices considering the regime shifts the crude oil prices were subject to during history. Models are evaluated in terms of their out-of-sample forecasting capabilities with equal forecast accuracy tests and also in terms of various error criteria. The results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently as compared to their single regime variants, such as the GARCH, FIGARCH and FIAPGARCH models. Further, the out-of-sample results suggest that the LSTAR-LST-FIAPGARCH model provides the best forecasting accuracy in terms of RMSE and MSE error criteria.
PSYCHOLOGICAL DOMINANCE, MARKET DOMINANCE AND THEIR IMPACTS IN TURKEY
(Editura Ase, 2015) Bildirici, Melike; Parasiz, Ilker; Ersin, Ozgur Omer; Aykac-Alp, Elcin
The study focuses on analyzing an economy that applies an inflation-targeting rule in which the policy interest rate is determined actively by the Taylor rule, and the policy maker involuntarily becomes the affirmant of inflation. In an economy that applies inflation-targeting policy where interest rates are determined in light of the Taylor rule, as the Central Bank tries to establish price stability and financial stability by determining policy interest rates, the Central Bank might fall into a position to do nothing but to assent inflation. In the empirical section, two new indices, the psychological dominance (pdi) and market dominance indices (mdi) are developed based on the difference between the policy rates. The band within which the indices follow random walk processes are determined with Band-TAR models. The CB policy is additionally modeled with a nonlinear Taylor rule with TVEC models. The most significant point of the process is its inflation-creating effect. By moving from the Turkey example, the main problem in the policies of Central Bank of Turkey is the difference between the borrowing and lending rates and its inflationary effect.
TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns
(Pergamon-Elsevier Science Ltd, 2010) Bildirici, Melike; Alp, Elcin A.; Ersin, Oezguer Oe.
The study aims to propose a family of Neural Networks (NN) model to achieve improvement in modeling nonlinear cointegration compared to Hansen and Seo (2002) Threshold Autoregressive Vector Error Correction (TAR-VEC) model. Our proposed TAR-VEC-NN family consist of TAR-VEC Multi Layer Perceptron (TAR-VEC-MLP), TAR-VEC Radial Basis Function (TAR-VEC-RBF) and TAR-VEC Recurrent Hybrid Elman (TAR-VEC-RHE) models. TAR-VEC-NN models are also discussed under two modeling strategies, first based on TAR-VEC modeling and the second based on a NN modeling approaches. The TAR-VEC-NN models proposed are analyzed for modeling monthly returns of TL/$ real exchange rate and ISE100 Istanbul Stock Exchange Index. For the data analyzed in the study, the TAR-VEC-NN models and their nonlinear cointegration structure improve forecast accuracy compared to TAR-VEC models; for both modeling strategies, we obtained similar results. Even though TAR-VEC-MLP model provides comparatively significant forecast improvement, TAR-VEC-RHE and TAR-VEC-RBF models achieve better forecast accuracy as expected given the dynamic memory structure of RHE and given the basis functions of RBF models which capture nonlinear error correction more efficiently. Further, our results show that, though with in sample accuracy, TAR-VEC-MLP and TAR-VEC-RHE produced the low RMSE values, in terms of long run predictions, the RBF model produced best results which is expected given the basis functions' capability in capturing deviations with the gaussian functions in a nonlinear error correction system. Thus, in the literature the forecasting ability of VEC type models are commonly criticized. With the use of our approach, there is an important improvement in VEC based models with NN specifications in terms of forecasts which cannot be disregarded. (C) 2009 Elsevier Ltd. All rights reserved.

Yazar "Bildirici, Melike" seçeneğine göre listele

Sayfa Başına Sonuç

Sıralama seçenekleri