Macro stress testing in Turkish banking sector and tail dependence in financial, energy and commodity markets

Abstract

This 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.