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Öğe Chaos, Fractionality, Nonlinear Contagion, and Causality Dynamics of the Metaverse, Energy Consumption, and Environmental Pollution: Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula and Causality Methods(MDPI, 2024) Bildirici, Melike; Ersin, Özgür Ömer; Ibrahim, BlendMetaverse (MV) technology introduces new tools for users each day. MV companies have a significant share in the total stock markets today, and their size is increasing. However, MV technologies are questioned as to whether they contribute to environmental pollution with their increasing energy consumption (EC). This study explores complex nonlinear contagion with tail dependence and causality between MV stocks, EC, and environmental pollution proxied with carbon dioxide emissions (CO2 ) with a decade-long daily dataset covering 18 May 2012–16 March 2023. The Mandelbrot–Wallis and Lo’s rescaled range (R/S) tests confirm long-term dependence and fractionality, and the largest Lyapunov exponents, Shannon and Havrda, Charvât, and Tsallis (HCT) entropy tests followed by the Kolmogorov–Sinai (KS) complexity measure confirm chaos, entropy, and complexity. The Brock, Dechert, and Scheinkman (BDS) test of independence test confirms nonlinearity, and White‘s test of heteroskedasticity of nonlinear forms and Engle’s autoregressive conditional heteroskedasticity test confirm heteroskedasticity, in addition to fractionality and chaos. In modeling, the marginal distributions are modeled with Markov-Switching Generalized Autore gressive Conditional Heteroskedasticity Copula (MS-GARCH–Copula) processes with two regimes for low and high volatility and asymmetric tail dependence between MV, EC, and CO2 in all regimes. The findings indicate relatively higher contagion with larger copula parameters in high-volatility regimes. Nonlinear causality is modeled under regime-switching heteroskedasticity, and the results indicate unidirectional causality from MV to EC, from MV to CO2, and from EC to CO2 , in addition to bidirectional causality among MV and EC, which amplifies the effects on air pollution. The findings of this paper offer vital insights into the MV, EC, and CO2 nexus under chaos, fractionality, and nonlinearity. Important policy recommendations are generated.Öğe Effects of Fiscal and Monetary Policies, Energy Consumption and Economic Growth on CO2 Emissions in the Turkish Economy: Nonlinear Bootstrapping NARDL and Nonlinear Causality Methods(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Bildirici, Melike; Genç, Sema Yılmaz; Ersin, Özgür ÖmerGovernments use fiscal and monetary policies to direct the economy toward economic expansion. However, both policies could have impacts on the environment. The study investigates the effects of fiscal and monetary policy, energy consumption and economic growth on carbon dioxide emissions for the Turkish economy from 1978 to 2021 with novel nonlinear bootstrapping NBARDL and nonlinear NBVARDL for nonlinear causality testing. The methods are robust to degenerate cointegration. By differentiating between expansionary and contractionary fiscal and monetary policies, the results determined the presence of long-run cointegrated relationships between the analyzed variables and emissions. The positive effects of both economic policies on emissions cannot be rejected, which become particularly pronounced for expansionary policies in addition to emission enhancing effects of energy consumption and growth. The effects of contractionary monetary policy are also positive in contrast to a set from the literature. Nonlinear causality tests favor one-way causality from energy consumption and from growth to emissions. The one-way causality from energy consumption and economic growth to emissions suggest non-existent feedback effects, leading to concerns for the environment. Expansionary and recessionary fiscal policies have one-way causal impacts on energy, leading to further environmental degradation. The findings highlight the severity of environmental problems caused by economic policies. Important policy recommendations are generated.Öğe Effects of Technology, Energy, Monetary, and Fiscal Policies on the Relationship between Renewable and Fossil Fuel Energies and Environmental Pollution: Novel NBARDL and Causality Analyses(MDPI, 2023) Bildirici, Melike; Çırpıcı, Yasemin Asu; Ersin, Özgür ÖmerThere is a body of research that focuses on the examination of long-run relations between energy–environment–economic growth, and there is also a new type of recent research that focuses on the effects of monetary and fiscal economic policies on the environment. There is a research gap that exists due to omitting the effects of technology and energy policies, and this paper addresses this gap, in addition to merging both fields mentioned above, by including the asymmetric effects of fiscal and monetary policies. To explore the relations between fossil fuel and renewable energies, environmental pollution, and economic growth, in addition to including the roles of energy, technology, monetary, and fiscal policies, this paper employs novel NBARDL and NBARDL Granger Causality methods for yearly data assessments in the USA. The empirical findings of the paper point to the asymmetric impacts of monetary and fiscal policies in the short- and long-run. Interestingly, both contractionary and expansionary fiscal policies lead to higher CO2 emissions. Contractionary monetary policies exert a downward pressure on CO2 emissions, and if expansionary, the monetary policy causes environmental degradation. As an important policy, the energy policy emerges as a potent tool for reducing carbon emissions through not only renewable energy, but as a greater impact through energy efficiency and technology. Therefore, this paper highlights the importance of technology policies exhibiting varying relationships with environmental pollution, featuring unidirectional or bidirectional causality patterns. Renewable energy, energy efficiency combined with adequate technology, and energy policies are determined to have pivotal roles in CO2 emissions outcomes. Such policies should focus on cleaner energy sources accompanied by energy efficiency technologies in the USA to curtail environmental impacts; technology policies are vital in fostering innovations and encouraging cleaner technologies. The policy recommendations include an effective combination of monetary, fiscal, technology, and energy policies, backed by a strong commitment to achieving energy efficiency and renewable energy to mitigate environmental pollution and to contribute to sustainable development.Öğe Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19(MDPI, 2023) Ersin, Özgür Ömer; Bildirici, MelikeForecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important; however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers.Öğe Forecasting BDI Sea Freight Shipment Cost, VIX Investor Sentiment and MSCI Global Stock Market Indicator Indices: LSTAR-GARCH and LSTAR-APGARCH Models(MDPI, 2023) Bildirici, Melike; Şahin Onat, Işıl; Ersin, Özgür ÖmerPrediction of the economy in global markets is of crucial importance for individuals, decisionmakers, and policies. To this end, effectiveness in modeling and forecasting the directions of such leading indicators is of crucial importance. For this purpose, we analyzed the Baltic Dry Index (BDI), Investor Sentiment Index (VIX), and Global Stock Market Indicator (MSCI) for their distributional characteristics leading to proposed econometric methods. Among these, the BDI is an economic indicator based on shipment of dry cargo costs, the VIX is a measure of investor fear, and the MSCI represents an emerging and developed county stock market indicator. By utilizing daily data for a sample covering 1 November 2007–30 May 2022, the BDI, VIX, and MSCI indices are investigated with various methods for nonlinearity, chaos, and regime-switching volatility. The BDS independence test confirmed dependence and nonlinearity in all three series; Lyapunov exponent, Shannon, and Kolmogorov entropy tests suggest that series follow chaotic processes. Smooth transition autoregressive (STAR) type nonlinearity tests favored two-regime GARCH and Asymmetric Power GARCH (APGARCH) nonlinear conditional volatility models where regime changes are governed by smooth logistic transitions. Nonlinear LSTAR-GARCH and LSTAR-APGARCH models, in addition to their single-regime variants, are estimated and evaluated for in-sample and out of-sample forecasts. The findings determined significant prediction and forecast improvement of LSTAR-APGARCH, closely followed by LSTAR-GARCH models. Overall results confirm the necessity of models integrating nonlinearity and volatility dynamics to utilize the BDI, VIX, and MSCI indices as effective leading economic indicators for investors and policymakers to predict the direction of the global economy.Öğe Industry 4.0 and Renewable Energy Production Nexus: An Empirical Investigation of G20 Countries with Panel Quantile Method(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Bildirici, Melike; Kayıkçı, Fazıl; Ersin, Özgür ÖmerIn line with the fourth industrial revolution, most countries have imposed a variety of regulations or policies for the goals of energy conservation, sustainable development, and industrial transition. Renewable energy production and its production process, which is widely discussed, espe cially in the context of sustainable energy, has become more important with Industry 4.0. This paper tested the relation among economic growth, renewable electricity generations (% of GDP), Industry 4.0, industrial structure, trade openness, financial development, and research and development expenditure for G20 countries in 2000–2021 by employing a panel quantile regression approach and various panel cointegration tests in addition to investigation of panel Granger causality among the analyzed variables. The variables of industrial structure, trade openness, and financial development were selected as control variables. Since this study is the first study on this topic, it will contribute to the development of the literature by providing resources for future studies about I4.0, renewable energy production, and economic growth. Furthermore, this study will not only contribute to the literature by revealing the theoretical and empirical relationship between these variables but will also shed light on the policies that G20 countries will produce in this regard. According to results, all variables examined have significant causal effects: unidirectional causality from economic growth to Industry 4.0, to research and development, and to renewable energy output and, also, from research and development to renewable energy output. Bidirectional causality and feedback effects between renewable energy and Industry 4.0 are determined. Further, unidirectional causality from industrial structure, from openness to trade, and from financial development to renewable energy output are determined. Results indicate renewable-enhancing effects of Industry 4.0.Öğe Nexus between Industry 4.0 and environmental sustainability: A Fourier panel bootstrap cointegration and causality analysis(Elsevier Sci Ltd, 2023) Bildirici, Melike; Ersin, Ozgur OmerThe paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy con-sumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns.Öğe PSYCHOLOGICAL DOMINANCE, MARKET DOMINANCE AND THEIR IMPACTS IN TURKEY(Acad Economic Studies, 2015) Bildirici, Melike; Parasız, İlker; Ersin, Özgür Ömer; Aykaç Alp, ElçinThe 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.Öğe Regime-Switching Fractionally Integrated Asymmetric Power Neural Network Modeling of Nonlinear Contagion for Chaotic Oil and Precious Metal Volatilities(MDPI, 2022) Bildirici, Melike; Ersin, Özgür ÖmerThis paper aims at analyzing nonlinear dependence between fractionally integrated, chaotic precious metal and oil prices and volatilities. With this respect, the Markov regime-switching fractionally integrated asymmetric power versions of generalized autoregressive conditional volatility copula (MS-FIAPGARCH-copula) method are further extended to multi-layer perceptron (MLP)-based neural networks copula (MS-FIAPGARCH-MLP-copula). The models are utilized for modeling dependence between daily oil, copper, gold, platinum and silver prices, covering a period from 1 January 1990–25 March 2022. Kolmogorov and Shannon entropy and the largest Lyapunov exponents reveal uncertainty and chaos. Empirical findings show that: i. neural network-augmented nonlinear MS-FIAPGARCH-MLP-copula displayed significant gains in terms of forecasts; ii. asymmetric and nonlinear processes are modeled effectively with the proposed model, iii. important insights are derived with the proposed method, which highlight nonlinear tail dependence. Results suggest, given long memory and chaotic structures, that policy interventions must be kept at lowest levels.
