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Yayın Determinants of Bitcoin price movements(Suat Teker, 2024-07-30) Teker, Dilek; Teker, Suat; Demirel, EsinPurpose- Investors want to include Bitcoin in their portfolios due to its high returns. However, high returns also come with high risks. For this reason, the volatility prediction of Bitcoin prices is the focus of attention of investors. Because Bitcoin's volatility is used as an important input in portfolio selection and risk management. This means that the models to be used in predicting Bitcoin volatility increases the importance of performance. In this research; A comparative examination of the models applied for Bitcoin shows an effective performance in volatility prediction. It is very important for evaluation. The aim of this study is to model Bitcoin price returns and to examine future return predictions and return directions using historical Bitcoin prices. Methodology- Many models have been used in studies on financial instruments and price predictions. Models such as linear and nonlinear regression, Random Walk Model, GARCH and ARIMA fall into this category. Nonlinear econometric models such as ARCH and GARCH are used for financial time series with variable volatility. These models assume that the variance is not constant. In this study, first Bitcoin price returns for the period between January 2020 and December 2023 will be modeled with the GARCH model, and then the ARCH-GARCH models will be used for future prediction of returns for the period between January 2024 and June 2024. Finally, the actual values will be compared with the forecasted values. In other words, the primary aim of this study is to use the daily Bitcoin closing price between May 2020 and December 2023 to estimate the returns for the periods of 2024 and compare it with the actual returns. Findings- The analysis reveals that GARCH Model results showed that in the mean and variance equations, it is seen that all variables are except intercept of the mean equation significant according to the error level of 0.05. Namely, the reaction and persistence parameters are significant accourding to 0.05 in the variance equation. Both the coefficient of the reaction parameter and the coefficient of the persistent parameter are higher than zero (positive). Also, the coefficient of the reaction parameter plus the coefficient of the persistent parameter approximately equals 0.72. That is, it is lower than 1 and higher than zero (positive). The level of persistence is not too high. So, we do not think about non-stationary variance in the model. Reaction parameter’s coefficient is 0.13. And persistence parameter’s coefficient is 0.58. As we can see, persistent parameter is much higher than reaction parameter. That is, when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. After determining the appropriate mean and variance models, a forecast is made using Automatic ARIMA forecasting for BITCOIN return forecasting. This forecast is made for the first five months of 2024, without adding the actual values of the first five months of 2024 to the data. The program ranks the most appropriate model. The program chose GARCH(3,3) as the most appropriate model in "bitcoin return prediction". Conclusion- The results of the test applied in the study can be summarized that the unit root test results showed that it was necessary to work with return series. GARCH(1,1) model results show when there is a new shock that creates the persistent parameter, that shock will be in effect for a long time, it will not disappear immediately. That is, a significant part of the shock that occurs in one period flows into the next period. According to GARCH automatic forecasting results, the best GARCH model that models Bitcoin return is the GARCH(3,3) model. According to these model results, although the slopes of the actual and forecasted return series move in the same direction, the model remains weak for forecasting. In future studies, it may be recommended to estimate Bitcoin returns with non-linear models.Yayın Export potential of Turkish SMEs(Suat Teker, 2024-07-30) Teker, Suat; Teker, Dilek; Orman, IrmakPurpose- Digital channels are gaining more and more share from trade and commerce, especially after Covid 19 pandemic. People have adopted to online buying and marketplaces became important retailing tools for manufacturers. E-commerce is rising not only in closed commercial areas but also across different countries, even continents with developments in cross-border e-commerce. Governments, global digital platforms, consumer habits are creating and supporting the demand of buying online from anywhere and numbers are showing that this creates an opportunity for Turkish businesses to become exporters. This study aims to highlight the potential for small and medium sized businesses in Turkey to become exporters. Methodology- The study examines historical export growth data of Turkey in detail using secondary data. The historical data is used to make a projection for future and highlight the potential of growth for Turkish SMEs. Current marketplace platforms’ business models are also examined and carefully analyzed to present an understanding of the potential business models. Findings- The numbers are showing that Turkish exports are growing in Europe and USA. Capex heavy industries have the highest share among the exports but e-commerce is also growing. Some industries like textile, jewellry and small appliences has a higher growth potential withing cross border e-commerce. Conclusion- Adoption to online retail is getting higher and higher. More people are buying from online marketplaces and the origin of the transaction is losing its importance with one-day deliveries. It is important to open shops not only physical but also on different platforms. It is easier for business owners to sell across the world and become exporters. By having international customers, businesses distribute regional risks and also become financially stronger. It is important for Turkish SMEs to understand their risks and seek international growth opportunities, such as doing exports. Turkey’s unique geographical location is a very important asset but Turkish businesses should keep in mind that all international producers are now seeking opportunities to create through online platforms.Yayın National income distribution: a countrywise analysis(Suat Teker, 2024-07-30) Teker, Suat; Teker, Dilek; Güzelsoy, HalitPurpose- This study aimsto analyze the changes in income distribution for selected developing countries over a time period in between 2015 and 2022, 8 years of observations. It hypothesizes that Covid19 pandemic period of 2020 and 2021 significantly impacted income distribution in all developing countries investigated. Methodology- Income distribution data for this study are extracted from the World Inequality Database addressing household income adjusted for after-tax income. Each household’s income is equally divided among the adult population aged 20 or older. The data are categorized into 10% income groups resulting in ten distinct income levels for the analysis. The study examines income distribution of five developing comprising Turkiye, Czechia, Greece, Hungary, and Romania. Findings- The top 10% of the population in the developing countries take 33% of national income on average. The average per capita income was $34,849 in 2015 and increased to $42,610 in 2022 after a dip of with a similar Covid19 dip. However, social policies generally failed resulting in income shifting from lower and middle-income groups to the top 30%. Conclusion- All countries implemented various social programs to support those most affected by Covid19. The social policies and measures implemented by governments to mitigate the effects of Covid19 appear to have been more successful in some of the developing countries comparing to the other developing countries. Although the developing countries could manage to increase their overall national income, they failed to restore their pre-pandemic income distribution. Significant income transfer occurred from the bottom 20% and middle 50% to the top 30% in these countries.
