Yazar "Kahraman, Umran Munire" seçeneğine göre listele

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    DETERMINATION OF FACTORS AFFECTING THE PROFITABILITY VARIABLES BY PANEL DATA ANALYSIS IN THE ISLAMIC BANKS: THE CASE OF TURKEY
    (Ilahiyat Bilimleri Arastirma Vakfi, 2020) Parlakkaya, Raif; Curuk, Suna Akten; Kahraman, Umran Munire; Sen, Gulsah
    Islamic banks, which operate on the profit and loss sharing basis, have an important role in the financial system in terms of the collected funds bringing into the real economy. Therefore, for a strong economic structure, the market share of Islamic banks in the financial system needs to increase. The profitability level of banks is one of the most important financial performance indicators. Determining the factors that affect profitability indicates which issues are vital. The aim of this paper is to determine the factors that affect the profitability of participation banks operating in Turkey. In this context, panel data estimation methods were applied by using the data obtained from the financial statements (2006-2019) of three participation banks (Kuveyt Turk, Albaraka ve urkiye Finans) and various macroeconomic indicators of the country. The most appropriate model was tried to be determined. In this study, the effect of capital adequacy ratio, bank size, credit risk, operational risk, operating effectiveness, inflation and GNP growth rate on return on assets (ROA) and return on equity (ROE) was analyzed. According to the results of the analysis, bank size, credit risk, operating effectiveness and inflation rates has an effect on ROA. Also, the effect of credit risk, operational risk, operating effectiveness and inflation rates on ROE is determined. Independent variables that do not have an impact on the profitability of banks are determined as capital adequacy ratio and growth. Also, according to the results of the analysis, it is possible to express that banks' specific variables are more effective on the profitability of participation banks than macroeconomic indicators.
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    Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
    (De Gruyter Poland Sp Z O O, 2023) Iyit, Neslihan; Sevim, Ferhat; Kahraman, Umran Munire
    Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of CO2 emissions due to the fuel types on percentage of deaths in total cases attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as percentage of deaths in total cases attributed to the COVID-19 pandemic calculated as (total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100. The explanatory variables are taken as production-based emissions of CO2 from different fuel types, measured in tonnes per person, which are coal, cement, flaring, gas, and oil. As a result of this study, according to the goodness-of-fit test statistics, GLMM approach with gamma distribution called gamma mixed regression model is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types cement, coal, flaring, gas, and oil per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.
  • Küçük Resim Yok
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    Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
    (De Gruyter Poland Sp Z O O, 2023) Iyit, Neslihan; Sevim, Ferhat; Kahraman, Umran Munire
    Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of CO2 emissions due to the fuel types on percentage of deaths in total cases attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as percentage of deaths in total cases attributed to the COVID-19 pandemic calculated as (total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100. The explanatory variables are taken as production-based emissions of CO2 from different fuel types, measured in tonnes per person, which are coal, cement, flaring, gas, and oil. As a result of this study, according to the goodness-of-fit test statistics, GLMM approach with gamma distribution called gamma mixed regression model is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types cement, coal, flaring, gas, and oil per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.