LEE_İktisat Lisansüstü Programı - Doktora
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Yazar "Güloğlu, Bülent" ile LEE_İktisat Lisansüstü Programı - Doktora'a göz atma
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ÖgeMacro stress testing in Turkish banking sector and tail dependence in financial, energy and commodity markets(Graduate School, 2024-02-07) Atik, Zehra ; Güloğlu, Bülent ; 412172007 ; EconomicsThis dissertation is a collection of three chapters on the resilience of Turkish banking system, nonlinear tail dependence from U.S. agricultural markets to Turkish agricultural markets, and nonlinear tail dependence from energy commodities to agricultural commodities. Financial institutions and financial markets exert significant effect on economies due to their critical role. When operating efficiently, they ensure the growth and stability of economies. Nevertheless, inefficiencies or vulnerabilities to shocks in these institutions and markets can result in systemic crises. For instance, deteriorations in the indicators of banks not only affect the banking system but also have repercussions across the entire economy through a feedback mechanism. As emphasized in the literature, higher nonperforming loans impede the real economy by slowing economic activity and credit growth. Financial markets, on the other hand, inherently contain the risk of financial contagion or spillover effects. While offering investment opportunities and facilitating price discovery, which contributing to wealth accumulation and economic advancement, they possess the potential to trigger joint collapses and substantial financial losses in the event of a shock. Thus, examining the vulnerabilities of financial institutions and the interdependence among various financial markets is exceedingly crucial, particularly considering the complexity of this interdependence. Accordingly, this dissertation evaluates the resilience of the Turkish banking system, the nonlinear dependence from US agricultural markets to Turkish agricultural markets, and the nonlinear dependence from energy commodities to agricultural commodities in separate chapters. The first chapter focuses on stress testing applied to conventional and participation banks within the Turkish banking sector. It employs the nonperforming loan ratio (NPL) as a pivotal stress variable. To conduct stress testing, the study uses an innovative additive nonparametric quantile regression technique. The objective is to assess how macroeconomic variables—exchange rates, interest rates, unemployment rates, and the public debt to GDP ratio—affect the NPL. The analysis covers monthly observations spanning from January 2005 to February 2020. Additionally, the chapter conducts scenario analyses based on various distributions and examines adverse scenarios concerning extreme tail values of macroeconomic variables. The study reveals the substantial influence of these macroeconomic factors on NPL, emphasizing the importance of stable exchange rates, controlled unemployment rates, and managed public debt. Furthermore, it highlights the necessity for tailored policies concerning different bank types, given observed disparities. The second chapter examines the relationship between U.S. and Turkish agricultural commodity markets, and also investigates the nonlinear tail dependence from Brent oil, USD/TRY currency rate, and overnight interest rate to Turkish agricultural commodities. It employs a novel nonlinear measure of tail dependence to explore the connection between these markets. The study analyzes the mean and tail dependence between the markets, considering both lower and upper continuum of quantiles. Additionally, it evaluates significant events such as the Turkish Mercantile Exchange (TMEX) launch and the Ukraine war. The dataset set contains daily returns from January 5, 2016, to May 31, 2022. To ensure robustness, a nonparametric test for Granger causality in distribution is employed. The findings reveal significant and persistent tail and mean dependence from U.S. agricultural futures to Turkish spot markets. This emphasizes the necessity for enhanced information exchange between spot and derivatives markets to strengthen market efficiency. The chapter also addresses the importance of monitoring interconnections and contagion risks among markets, especially during challenging market conditions. The third chapter investigates the impact of energy commodities—Brent oil, natural gas, and gasoline—on agricultural commodities. It employs a nonlinear measure of tail dependence to analyze the relationship across three distinct periods: pre-Covid-19, during Covid-19, and post-Covid-19. The dataset comprises daily records from June 1, 2017, to June 9, 2023, covering different market periods. Additionally, the chapter utilizes cross-quantilogram analysis and the nonparametric test for Granger causality in distribution for robustness checks. The research reveals significant and persistent lower and upper tail dependence between energy and agricultural commodities across each period. The findings demonstrate nuanced and dynamic tail dependence patterns, indicating asymmetric risk transmissions under varying market conditions. These results have critical implications for effective market strategies, efficient portfolio management in agricultural commodity markets, and the need for resilient regulations during extreme events.
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ÖgeNetwork neutrality in the internet as a two sided market constituted by congestion sensitive end users and content providers(Graduate School, 2023-03-01) Kaplanlıoğlu, Özgür ; Güloğlu, Bülent ; Ecer, Sencer ; 412152004 ; EconomicsIn this paper, I envision a two-sided market mediated by a monopolistic internet service provider, ISP. The ISP provides end-users internet access and carries content providers' (CPs') data packages on its network. I compare the case where network neutrality is strictly practiced with the case where the ISP can "throttle" the traffic of certain content providers. In the model, for simplicity, a single CP is exposed to throttling, while the other CPs, which are part of a continuum, are not. I then study the implications of the violation of network neutrality on total data consumption, congestion, and capacity investment. Under network neutrality, the decision variables of the ISP are end-user price and network bandwidth. I found that, in equilibrium, because of the monopolistic nature of the market is greater than the price under competitive equilibrium, and is lower than its socially optimum value. Thus, under neutrality, the ISP undersupplies both the capacity and the data. Under discrimination, the ISP is allowed to charge an access fee on unit bandwidth to one of the content providers (the discriminated CP). To reflect a scenario of great practical value, I choose the discriminated CP from one of the big OTTs such as YouTube, Instagram, Facebook, Netflix, etc. In this setting, to access the network, the discriminated CP needs to buy bandwidth from the ISP. However, the bandwidth bought by the discriminated CP is not for exclusive usage of the discriminated CP. It rather acts as an upper bandwidth limit for the discriminated CP. Under discrimination, the decision variables of the ISP are the end-user price, the network bandwidth, and the bandwidth price. I found that when allowed the ISP always prefers to deviate from network neutrality by charging a positive price for bandwidth. Also, the ISP sets just enough to keep the discriminated CP in the market. Comparing the equilibrium outcomes, I show that under discrimination, the ISP charges a lower price to end-users. However, the discrimination also leads to less network bandwidth installed. Both the lower end-user price and the lower network bandwidth contribute to the congestion. Thus, under discrimination, the congestion is higher than under neutrality. Considering its adverse effects on the network bandwidth and congestion, although the end-user price is lower under discrimination, I recommend that the network neutrality principle should not be abolished.
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ÖgeSign predictability of intraday price returns to formulate appropriate trading strategies with optimum set of equities(Graduate School, 2024-02-08) Kılıç, Abdurrahman ; Güloğlu, Bülent ; 412162001 ; EconomicsThe prediction of stock market returns has been a focused research area for computational finance, time-series econometrics, and computer science researchers. Typically, market participants rely on technical and fundamental analyses to make predictions, determining their buy or sell strategies based on these assessments. However, scholars in finance and economics have remained skeptical about the predictability of stock returns, particularly in efficient markets. Besides the stock market earnings, a literature on capital martket's direciton predcition has been emerged. Studies in the field of computer science also supported this research area with the help of machine lerning, deep lerning and artifical neural network techniques. Empirical studies in applied economics focuses on efficient market hypotesis whereas computer science studies mostly compares prediction performances of diffirent techniques. In this thesis, closing price direction of 26 stocks in 5 miutes intervals predictited. Only the higly liquid stocks existed in the BIST 30 index for the entire year of 2018 are examined. The data includes 251 trading days and 84 data nodes for each day. Transaction costs are considered to be 2.5 bps and 15 bps is determined as the treshold for a direction signal. If 5 minute closing price of an equity went up by 0.15 percent, it is tagged as "positive" and the opposite as "negative" and the inbetween in considered "steady" to achieve economically significant results. A significant data source—Borsa Istanbul's 'data analytics' information distributed through data vendors—is utilized. There are 39 analytics providing order book statistics regarding for last 1 minute, 5 minute and intraday periods. Initially, a specific method for extracting valuable insights from the complex raw data of Borsa Istanbul is described and implemented. Subsequently, standard scaling and dimension reduction methods, such as Principal Component Analysis, are employed to enhance efficiency. Seven different machine learning algorithms, Logistic Regression, K-Nearest Neighborhood, Support Vector Machines with radial and sigmoid kernels, Naïve Bayes, Decision Tree, Random Forest, are compared. All the methods trained with former 95 percent and tested using the last 5 percent of the complete dataset of the year 2018. The performance levels of each method for twenty-six highly liquid stocks are assessed in terms of Macro Averaged F-Measures. Furthermore, in this thesis, the effectiveness and significance of machine learning algorithms are compared using confidence intervals calculated from the confusion matrices for Macro Averaged F-Measures—an innovative approach within the economics and finance literature. Additionally, the use of three classes for stock price directions 'positive ,' 'negative" and 'steady' rather than just two is a rare aspect in academic studies, aligning more closely with data analytics practitioners' methodologies. Concerning the 5-minute lagged data, a statistically significant predictability is found in nine of the equities. K-Nearest Neighbors, Decision Tree and Random Forest yielded significant predictions. Naïve Bayes and SVM-sigmoid achieved better for a few equites while Logistic Regression and SVM-rbf not for any. However, for the 10 and 60-minute lagged data, predictability remains only in four and none of the equities, respectively. Essentially, markets assimilated Borsa Istanbul's data over time for those equities. Moreover, economic gains for the nine equities are analyzed with algorithms not allowing short selling and allowing short selling, based on these predictions. 'Positive", 'negative' and 'steady' signs treated as 'buy', 'sell' and 'keep the position' signals in the first scenerio while 'steady' allowed as the 'short-sell' signal in the second scenerio. Those scnerios are compared with passive buy and hold strategy and it has been revealed that determined trading strategies depending on the machine lerning algorithms performs higher earnings or less losses for KOZAA, KOZAL and KRDMD stocks when short selling is not allowed. If short selling is allowed TKFEN is also added to this list. Test period performances of the determined trading strategies and the stocks' own price performances are graphically illustrated in the thesis. Since four equities are observed to generate higher economic gains through machine learning-supported trading strategies compared to their own price performances, these findings for those wquities indicate, within the framework of the 'Efficient Market Hypothesis,' a lack of 'Semi-strong-form efficiency.' The methods and techniques used in this study such as creating tree dimensional confusion matrices, determining standard deviation and confidence intervals for MA F-Measures, and benchmarking them with a theorical MA F-Measure and developing trading strategies with three signals will support further research
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ÖgeThe digital divide in Turkey(Graduate School, 2023-04-05) Dalgıç Tetikol, Deniz Ece ; Güloğlu, Bülent ; 412162004 ; EconomicsInformation and communication technologies (ICT), which broadly include telecommunications, mobile telephony, the Internet, and various Internet-enabled devices, have permeated most aspects of life by offering new and more efficient ways for people to communicate, access information, and learn. With applications in education, banking, e-commerce, health, and government services, among other areas, ICT are a major force behind economic growth and productivity, connecting people to essential services and jobs while enabling businesses to operate. The digital divide describes the uneven distribution of ICT in society. At a high level, the digital divide is the gap between those who have and do not have access to the Internet. However, the digital divide is not a binary but multifaceted and includes many factors such as connectivity, access to equipment, affordability, quality of service, motivation to use the technology, digital skills, and so on. Therefore, the digital divide encompasses various Internet-related challenges, which results in different types (levels) of digital inequality. The digital divide literature categorized those challenges as follows: differences in access (first-level digital divide), usage and digital skills (second level digital divide), and outcomes of Internet use (third level digital divide). These digital gaps exist at the international level as well as within a country. Often these gaps fall along other social inequalities in a country – that is, the different levels of the digital divide usually reflect the gaps between individuals from different demographic backgrounds and at different socioeconomic levels with regard to their opportunity and ability to access and utilize ICT. This thesis empirically examines the different aspects of the digital divide in Turkey. It explores the demographic and socioeconomic determinants of the first level and second level of digital divide in Turkey and analyzes the data that substantiates digital inequalities between years 2008-2020. Digital transformation and technological advancements in ICT offer tremendous opportunities for countries, especially for emerging economies. However, the full potential of digital advancements cannot be achieved by focusing solely on supply-side policies such as investing in infrastructure deployment. Despite increased Internet penetration rates, Turkey has failed to create a digitally inclusive society and risk missing out on the benefits of digitization. One of the leading factors contributing to this problem is the lack of effective demand-side solutions mitigating the digital divide. The results of this thesis suggest that significant disparities persist between different social groups in Turkey in terms of Internet access and Internet usage patterns. The findings of this thesis point out the target groups of first priority to address the first level and second level digital divide in Turkey. We can conclude from the results that special initiatives and programs are required to increase widespread adoption of the Internet. Those initiatives and programs should be designed and implemented with a participatory approach, targeting the high priority groups: women, older citizens, citizens with lower household income, citizens with low educational background, homemakers, and retired people, as well as citizens of the Northeast Anatolia, Central East Anatolia, Southeast Anatolia and West Black Sea regions of Turkey in particular, while considering the factors that make Internet access and use difficult.
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ÖgeThe impact of economic and financial shocks on macroeconomic fundamentals: Multi country new Keynesian approach(Sosyal Bilimler Enstitüsü, 2020) Güngör, Mahmut Sami ; Güloğlu, Bülent ; 671998 ; İktisatThis dissertation examines the impact of various economic and financial shocks on macroeconomic fundamentals with the help of a multi country modeling approach for the post 1990 era. Its theoretical framework is entirely based on the new Keynesian synthesis. In the first empirical section, I estimate two distinct versions of the three equation new Keynesian model by using the Bayesian techniques to investigate the impact of structural shocks on macroeconomic fundamentals under aggressive monetary policy in Turkey for the period from 2000Q1 to 2019Q1. The practical role of this chapter is to introduce the basic new Keynesian model which forms the fundamental structure of modern macroeconomic models. In the second empirical section, a single country framework is extended to a multi country one to investigate transmission of shocks across economies. First, I define the major trading partners of Turkey to construct a new globe for the multi country new Keynesian analysis. Next, I estimate the global VAR model for the period from 1990Q1 to 2018Q4 in order to obtain steady state values for all variables used in the multi country analysis. Then, I estimate the multi country new Keynesian model for the member countries of the new globe by using the inequality constrained instrumental variables estimator for the period from 1990Q3 to 2018Q4. Finally, a number of plausible economic and financial scenarios are discussed by using both point and bootstrap estimates of impulse responses of related shocks in the dynamic analysis part.