Bu çalışma Dolar/TL döviz kuru volatilitesini modellemeyi amaçlamaktadır. Bu doğrultuda çalışmada geleneksel değişen varyans tekniklerinin yanı sıra zaman serilerinin kırılma ve asimetri gibi doğrusal olmayan özelliklerini dikkate alan Markov Rejim Değişimi GARCH (MSGARCH) yöntemi kullanılmıştır. Çalışma kapsamında döviz kuru volatilitesi Temmuz 2001’den Şubat 2020’ye kadar uzanan konjonktürel dalgalanmaları ve yapısal değişimleri içeren uzunca bir dönem için incelenmiştir. Diğer yöntemlere göre daha iyi sonuçların elde edildiği MSGARCH modeli bulguları kurda yüksek ve düşük riskli rejimler olduğunu ve bu rejimler arasında sıklıkla geçişler yaşandığını göstermektedir. Düşük volatilite rejiminden yüksek volatiliteye geçiş ani, çok keskin ve yüksek düzeyde gerçekleşmektedir. Buna rağmen volatilitenin yüksek riskli rejimde kararlı bir yapıda olmadığı ve riskin daha düşük olduğu rejime doğru bir eğilim sergilediği tespit edilmiştir. Döviz kurunda yaşanan ani sıçramaların bir süre sonra dengeye gelmesi bunu doğrulamaktadır. Diğer taraftan düşük riskli rejim dönemlerinde ise meydana gelen dalgalanmaların etkileri ortadan kalkmayıp kur üzerinde bunlar korunmakta ve dolayısı ile kur uzun dönemde bir artış trendi göstermektedir.

In today’s world, carbon emissions, energy consumption, and economic growth are an issue of interest for all countries that want to continue growing by establishing a balance between energy and carbon emissions. This brief research investigates the relationship between carbon emissions, energy consumption and economic growth using data between the years 1997-2014 period for Turkish and Georgian economies. Panel data cointegration tests are carried to analyze the relationship between variables. The overall finding results indicate that there is a significant relationship between the variables for the Georgian and Turkish economies. Moreover, the tests results are consistent with the general assumption of the Environmental Kuznets Curve hypothesis.

The concept of FinTech, which is the combination of finance and technology, aims to facilitate many services offered by financial institutions. This chapter examines the development of FinTech practices in terms of Islamic finance in Turkey. The chapter assesses the viewpoints of the sector participants, including participation banks, corporate customers, and FinTech companies operating in Turkey, about Islamic finance and FinTech. In this regard, the ultimate goal of the study is to present a projection to the market actors and decision makers in order to be able to enhance the share of Islamic finance in the sector. The interview findings of the study indicate that (1) new technological products and services will dominate the banking sector and these will change the way of doing business in the near future; (2) perception, knowledge, infrastructure, service network, and campaign factors affect the Islamic banking preferences of the customers; and (3) the challenges of legal acts, confidence, competition, and human resources primarily influence the development of the financial ecosystem.

Araştırma ve Geliştirme (Ar-Ge) faaliyeti, yaratıcılık ve yeniliklerin ortaya çıkmasını sağlar. Bu sayede, değişimler meydana gelir. İşletmelerin rekabet üstünlüğü kazanmalarına ve sürdürülebilir bir büyüme ivmesi yakalamalarına katkıda bulunur, başarılı bir finansal performans göstermelerinde önemli bir rol oynar. Bu doğrultuda araştırmada Ar-Ge yatırımlarının 2010-2019 yılları arasında BIST TUM endeksinde işlem gören ve 9 farklı sektörde faaliyet gösteren 18 şirketin finansal performanslarına etkisi Veri Zarflama Analizi ile incelenmiştir. Analiz sonucunda orman ürünleri-mobilya sektöründe yer alan GENTS şirketinin ve madencilik sektöründe yer alan KOZAA şirketinin en yüksek etkinlik düzeyine; otomotiv sektöründe yer alan OTKAR şirketinin ve gıda sektöründe yer alan ULKER şirketinin ise en düşük etkinlik düzeyine sahip oldukları tespit edilmiştir.

Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE, and R². The results indicate that greater algorithmic complexity does not necessarily improve forecasting accuracy. In several cases, the parsimonious SVM model outperforms deep neural network architectures, particularly for highly volatile assets, while hybrid models fail to provide systematic improvements and sometimes amplify prediction errors. SHapley Additive exPlanations analysis further shows that immediate price-based variables dominate predictive power, whereas many lagged technical indicators contribute relatively limited explanatory value. Overall, the findings underscore the importance of algorithmic parsimony, suggesting that simpler machine learning models may deliver more robust forecasts in highly volatile cryptocurrency markets.

The agricultural sector, which has long been overshadowed by industrialization, has reemerged with renewed strategic significance in the face of global crises, including pandemics and armed conflicts. This study examines the causal relationship between institutional quality and agricultural credit orientation in the Developing-Eight countries from 2002 to 2023. Using the agriculture orientation index for credit as a key indicator, this study investigates how disaggregated institutional dimensions—control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability—affect the allocation of commercial bank credit to agriculture. Both the standard Kónya panel causality test and its time-varying extension are employed to capture static and dynamic causal patterns. The findings demonstrate that institutional quality exerts a substantial effect on credit orientation, although the magnitude and characteristics of this influence differ across countries. Türkiye, Indonesia, Nigeria, and Egypt exhibit consistent causal relationships, whereas other countries reveal episodic or latent effects linked to specific political or legal shifts. By combining dynamic methodology with a policy-relevant indicator, this study offers novel insights into how governance shapes agricultural finance. The results underscore the need for country-specific and institution-sensitive credit strategies to increase resilience and equity in financial systems.