I am a financial econometrician and PhD candidate in Business Administration at Istanbul Technical University. I currently work as a Research and Teaching Assistant in Finance at Yeditepe University. My academic background combines econometrics, finance, and applied economic analysis, with a strong focus on empirical research and quantitative modeling.
My research examines financial market dynamics under uncertainty, crisis periods, and structural regime changes. I specialize in time-series econometrics and nonlinear modeling approaches, particularly regime-switching models and volatility analysis. My recent work focuses on financial sustainability, exchange rate volatility, inflation dynamics, and risk behavior across financial markets.
I have experience in academic research projects, conference organization, and teaching support in finance, statistics, and econometrics courses. My research aims to bridge econometric methodology with real-world financial applications, especially in emerging market economies.
I am interested in international research collaboration, special issue submissions, and interdisciplinary academic projects in finance and applied econometrics.
Purpose- Gender differences in investment behavior have been reported by various studies. Behavioral investing seeks to bridge the gap between psychology and investing. Behavioral finance is becoming more predominant in the financial and investment industry. The general concept of behavioral finance suggests that investors do not necessarily make rational investment decisions. Many results of behavioral finance studies show that men and women have different strengths and weaknesses in terms of skills required for investment management. This study focuses on the role of gender in risk perception and investment behavior, with a sample size of 288 respondents. In other words, the aim of the research is to reveal whether there is a difference in investment preferences between men and women. It is investigated whether the gender factor affects investment decision-making behavior. Using an experimental finance approach, the relationship between gender diversity and investment decisions is examined. Methodology- This study focuses on the role of gender in risk perception and investment behavior, with a sample size of 288 respondents. Gender differences in investment behavior have been reported by various studies. Behavioral investing seeks to bridge the gap between psychology and investing. Behavioral finance is becoming more predominant in the financial and investment industry. The general concept of behavioral finance suggests that investors do not necessarily make rational investment decisions. In accordance with the aim of the research, to reveal whether there is a difference in investment choices between men and women, the investment differences between the genders are shown using the graphic method in this study. Then, the normality test and Mann-Whitney U test were applied by using 288 respondents, respectively. Findings- According to the graphic method results it is found that women generally prefer to invest between 10% and 25% of their monthly income in financial markets. T cryptocurrency market is riskier than the stock market for both women and men. Women experience more stress than men at the thought of losing money because of their investment choices. The Cronbach Alpha coefficient for estimating the reliability of the scale employed for respondents’ investment preference was found to be 0.701. The results of data processing obtained by the value of the Kolmogorov-Simirnov significant which means the data were not normally distributed residuals. According to Mann-Whitney U test results, it is underlined that the gender factor differs according to the following variables based on 95% significance level: Conclusion- Survey with different aspects of questions focus on investors’ risk perception. “How often do you check your investments?”; “What is your approximate holding time of an investment instrument?”; “What percentage of your monthly income would you prefer to invest in financial markets?”; “The thought of losing money because of my investment choices is stressed me out”; “Have you ever invested in Cryptocurrencies?”; “What is the most suitable option for your knowledge of the cryptocurrency market?”. It is concluded that there is a significant difference between gender and investment preference. Keywords: Behavioural finance, financial market, cryptocurrency, gender factor, risk perception. JEL Codes: G10, G40, G41
The aim of this study is to determine the number of transactions among the currencies, which will eventually become a part of our lives, cannot be physically held, can move quickly, and emerge as a new shopping and investment tool in the changing world order, as of the year (2023) when this study was conducted. The study focuses on the analysis of the variables that affect the most popular currency, Bitcoin. Although the analysis of variables that influence Bitcoin was determined as the primary aim of the study, the study also attempted to reach a general conclusion about the variables affected by the cryptocurrencies. Since there is no other cryptocurrency that is traded as much as Bitcoin, Bitcoin is thought to be a good model for the analysis of cryptocurrencies. The method used in the study was autoregressive conditional heteroskedastic (ARCH) models. It is believed that the most suitable models for the Bitcoin variable, whose value changes every second, are ARCH and its derivatives. Other models selected from the ARCH models were also added to the analysis as a method. The models used in the study can be listed as follows: linear ARC, generalized ARC (GARCH), exponential GARCH and threshold GARCH. A statistical model called autoregressive conditional heteroscedasticity (ARCH) is used to study the volatility of time series. Through the provision of a volatility model that more closely mimics actual markets, ARCH modeling is utilized in the financial sector to quantify risk. According to ARCH modeling, periods of high volatility are followed by even higher volatility, and periods of low volatility are followed by even lower volatility. In this study, 5 different variables were selected using literature to analyze the variables affecting Bitcoin returns using ARCH models. The dependent variable in the study is the price of Bitcoin. The remaining variables were included in the models as independent variables. These variables are actually variables that are accepted and selected as the best among a set of variables. In other words, 15 variables were first added to the study using the literature. After this, a correlation analysis was carried out. As a result of the correlation analysis, the variables with the highest correlation with the price of Bitcoin, which is the dependent variable, and the lowest correlation with each other were retained in the model. These variables are Bitcoin Price, Crude Oil Spot Price, Euro-Dollar Parity, Gold Spot Price and NASDAQ Composite Index. The study period is between 2020 and 2023 and it was studied using daily data. Days with no data were removed from the daily period from 2020 to 2023 and loss of information was prevented. After removing missing observations, this study examined the remaining 837 observations. During the research, while running the models created using different methods, it was found that the model that gives the best result is the GARCH model. In other words, when modeling the variables affecting bitcoin (cryptocurrency from the perspective of the population), it was seen that the GARCH model gave the best results when comparing linear ARCH, generalized ARCH (GARCH), exponential GARCH, and threshold GARCH of the ARCH model. Comparing the output of the GARCH model with other ARCH models not included in this study can be a recommendation for the future study
Purpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoin’s volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoin’s price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoin’s volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy. Keywords: Bitcoin, ARCH models, GARCH Models, forecasting, ARIMA models JEL Codes: C58, G10, G12
Purpose- 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. Keywords: Bitcoin, ARCH models, GARCH models, forecasting, ARIMA models JEL Codes: C58, G10, G12
Purpose- Family firms have a significant economic role in many countries around the world. Family firms make a significant contribution to World GDP and employ a significant part of the global workforce. The scope of this study covers the top 25 largest and publicly owned family firms announced by Ernst & Young’s 2021 Report for Family Businesses. These 25 family firms generated more than 2 trillion USD and employed 6.5 million in 2021. This empirical study aims to investigate the excess stock returns of family firms over the related country stock market indexes and the risks (betas) for the period between 2002 and 2021. Therefore, this study explores the question of “why invest globally in family firms and whether this investment pays off with higher returns and less risk”. Methodology- The World's Largest Family Companies" list is published every other two years by Ernst & Young and the last issue was published in 2021. The world’s largest family companies list includes both private and publicly owned family firms. This study employs 25 world’s largest family firms after the exclusion of privately held family firms. The monthly stock prices of family firms, related country stock market index values, and global stock market index values are obtained from Refinitiv Eikon (Reuters) database for the period between 2002 and 2021 (20 years). Therefore, a total of 9120 observations are extracted for this empirical study. Eviews-10 is utilized for all econometric analysis. Findings- This study investigates whether an individual or intuitional investor can earn more than the average return of the stock markets by investing in publicly traded family firms meanwhile exposing less risk. The empricial results reveal that Maersek shows 354% (beta of 1.18) excess return over the 20 year period and followed by Hanwha with a 335% (beta of 0.69) excess return. Later, all family firms are grouped based on country of headquartered and 7 country portfolios are formed. The highest excess returns are provided by South Koean portfolio (an excess return of 189% with a beta of 0.83) and it is followed by Indian portfolio (an excess return of 174% with a beta of 1.0). Finaly, a best performer portfolio is formed by the 10 family firms with highest excess returns. This portfolio provides a 131% excess return with a beta of 1.18 over 20-year peirod. Conclusion- The empirical results show that the individual firm returns and portfolio returns of family firms are higher than the returns of the stock market indexes. Those who invest in family businesses get higher returns with less risk. Investments in publicly traded family firms pay off. Keywords: Family firms, global investment, return, risk, stock markets. JEL Codes: G10, G11, G15
Purpose-Capital Asset Pricing Model (CAPM) is the most widely used and popular method in analysis of investment projects, stock valuation, firm valuation, mergers and acquisitions, initial public offerings and secondary public offerings. The determination of market risk premium is one of the most important inputs in the application of this model. The determination of market risk premium for the Turkish market has not deeply studied in the literature so far. This study intends to calculate the market risk premium for the Turkish Stock Market with a special emphasis on the Covid-19 era. Methodology-The monthly data from the Reuters Database are collected for the BIST100 and 17 different sectoral indexes for the years of 2019 and 2020. Moreover, the monthly average short term interest rates on the Turkish Treasury Bonds are obtained from the database of Central Bank of Turkey for the years of 2019 and 2020. Based upon the historical observations, the market risk premium is defined as the difference in between the market index returns (BIST100 and 17 sectoral indexes) and the average short term interest rates on monthly basis. Findings-The market risk premiums measured on BIST100 index are about 10% in 2019 and 20% in 2020. The market risk premium is doubled in the Covid era. The volatilities of BIST100 index are 7.86% in 2019 and 8.15% in 2020. The volatility of market risk premiums are also significantly increased in the Covid era. Conclusion-Covid era has significantly increased the market risk premiums and volatilities of the Turkish market. The results of this study may be used as a reference study for local and international financial institutions, valuation industry and trade firms and academics for an approximation of market risk premium in the Covid era.
Purpose- This empirical study aims to measure the sectoral market risk premiums in the Turkish stock market for the period of 2016 and 2021 and also estimate the sectoral market risk premiums for the years 2022, 2023 and 2024. Capital Asset Pricing Model (CAPM) is the most widely used and popular method in the analysis of investment projects, stock valuation, firm valuation, mergers and acquisitions, initial public offerings, and secondary public offerings. The market risk premium in CAPM is defined as the the difference in between expected market returns and interest rates. The determination of market risk premium is one of the most important inputs in the application of the CAPM. This study intends to calculate the market risk premiums and volatilities for the sectors of Borsa Istanbul for the periods of pre-Covid (2016-2017-2018) and in the Covid-19 era (2019-2020-2021). Methodology- The monthly data from the Reuters Database are collected for the BIST100 and 17 different sectoral indexes and short-term interest rates between the years 2016 and 2021. A total of 1296 observations are obtained. Based upon the historical observations, the market risk premiums are defined as the difference between the market index returns (BIST100 and 17 sectoral indexes) and the average short-term interest rates on monthly basis. Then, using the ARIMA forecasting method, the market risk premiums are estimated for the years 2022, 2023, and 2024. A total of 576 data points are forecasted. Findings- The average risk premium on the BIST100 index is about -2.44% for the pre-Covid era and 14.01% for in-Covid era. The market risk premiums sharply increased from the pre-Covid period to the Covid period. The average volatility on the BIST100 index is about 0.23% for the pre-Covid era while 0.34% in the Covid era. The volatility of the market returns also incresed significantly. Moreover, the Cusum Square Test results point a structural break in the Covid-era. The ARIMA estimates of market risk premiums are 1.87% for 2022, 0.43% for 2023 and 0.42% for 2024. THe ARIMA estimates of volatilities are 0.70% for 2022, 0.72% for 2023 and 0.71% for 2024. Conclusion- The empirical evidence strongly support a structural change in the Covid era with higher market risk premiums and volatilities. The forecasted market risk premiums for the next three years show a diminishing trend while the forecasted volatilities show high and persistent level. Keywords: Market risk premium, BIST100, ARIMA forecasting, sectoral market risk premiums, volatility. JEL Codes: G10, G12, G17
This study aims to show the similarities and differences between the <em>fragile five classifications</em>, which include countries that are quite different from each other, and to show whether there is a need for a different classification of fragile five, econometrically. In this context, the data set consists of <em>old fragile five</em> and <em>new fragile five</em> classifications. Seven independent variables that are thought to affect the gross domestic product of the countries have been determined. The data are annual for the period 2001-2018. Panel data analysis and Panel vector autoregression are used as a methodology in this paper, respectively. As a result of the analysis, the effects of the independent variables used in the analysis on the dependent differ in the countries included in <em>fragile fives</em>. Also, a change in one of the countries included in <em>fragile fives</em> will affect other countries. Therefore, it concluded that the variables in the models of <em>fragile fives</em> generally have different coefficients from each other. Based on this, it is understood that the revision of <em>old fragile five</em> does not conform to <em>new fragile five</em>, econometrically. It can be suggested as a policy implication that a different classification of fragile five is necessary.
Purpose- The cognitive comprehension of financial indicators, risk aversion, risk perception, and investment behavior is defined as financial literacy. It's possible that a variety of characteristics, such as gender, age, income level, social standing, education, etc., will affect an investor's behavior. The purpose of this study is to highlight the behavior of investors in Turkish capital markets. The analysis is done on the results of two surveys, the first conducted in the fourth quarter of 2022 and the second in the first quarter of 2023. Methodology- This study's objective is to highlight investor behavior and risk perception in Turkish financial markets. In the most recent two consecutive quarters, the results of two surveys are analyzed and compared. Three sections comprise the surveys. A demographic question is asked in the first section. The second section asks questions concerning investment behavior, signs of financial stress, and confidence in regard to one's financial literacy. The final aspect contributes to the analysis of what people think of the Bitcoin market. In this study, Graphic analysis, Cronbach Alpha, Normality, and Mann-Whitney U tests are performed, respectively. First, the graphical analysis of the selected questions is made. Based on these graphs, the similarities and differences between the surveys are shown. Second, The reliability test is applied to the selected questions for the statistical modeling of the analysis. This test is determined as the Cronbach Alpha test. Third, the Normality test is applied to reveal which test to use in the next step. Two different tests are used for this analysis. These are the Kolmogorov-Smirnov and Shapiro-Wilk tests. Fourth, the Mann-Whitney U test is applied. At this stage, firstly, Mann-Whitney U and Wilcoxon W test statistics are examined. The ranks are calculated for each variable. Finally, the Mann-Whitney U test is applied, and the results are interpreted. Fifth, The results of the two surveys are compared. Findings- The findings show both similarities and differences among numerous variables. For instance, holding time is defined as the amount of time an investor holds an investment or as the time between purchasing it and selling it. Investors' risk aversion and financial literacy both influence the holding period. Riskier assets force investors to adjust their purchase or sell actions dynamically. The results show various portfolio diversification behaviours. While men prefer to start with foreign currency investments, women are more interested in making gold investments. Also, middle-aged investors invest more in cryptocurrencies and take more risks than younger investors. Conclusion- based upon the analysis, findings it may be concluded that respondents do differ in their investment preferences and risk-taking over the years. The findings show various portfolio diversification behaviors. While men prefer to invest in foreign currency, women are more interested in purchasing gold. Keywords: Investor behavior, risk perception, cryptocurrency market, Bitcoin, Mann-Whitney U test. JEL Codes: G1, G4, C4
The financial sector, which has sparked increasing organizational and scientific interest in recent years, plays a vital role in the Turkish economy. After enduring multiple economic downturns, consumers have become more cautious when considering financial investments, making it challenging for financial institutions to formulate effective marketing strategies. This study aims to shed light on investor behavior in Tukish markets. The results of two surveys are examined: the first conducted in the final quarter of 2022, and the second in the first quarter of 2023. This article delves into various variables, including stress levels, portfolio holding times, investment choices, and attention to cryptocurrency markets. The methodology employs the Mann-Whitney U test, Cronbach's Alpha, Kolmogorov-Smirnov, and Shapiro-Wilk normality tests. The findings from the two surveys are compared. Based on the analysis results, it can be inferred that respondents' investment preferences and risk tolerance have evolved over time. The results demonstrate a spectrum of portfolio diversification tendencies.
Purpose- Exchange rate is the value of a country's national currency against foreign national currencies. In this context, the exchange rate is considered an important macroeconomic indicator in evaluating the country's economy. The failure to control the exchange rate may damage economy significantly. It is possible to understand this from the 2001 crisis in Turkey, known as 'Black Wednesday', and the foreign exchange crisis that started in Thailand in 1997 and affected many East Asian countries. Interest rate is one of the critical determinants affecting the exchange rates. Therefore, changes in interest rates are expected to affect the level of exchange rates. When there is an increase in interest rates, foreign capital flow is expected for that particular country. Hence, a decrease in exchange rates is expected for the excess capital flows. This study aims to analyze the relationship between exchange rates and interest rates, considering the last 10 announcements of the interest policy of the Central Bank of the Republic of Turkiye. These announcements are between January 19, 2023 and October 26, 2023. The study used the TL/USD exchange rates and 10-year government bond interest rates to measure the relationship in between these two variables. Methodology-The aim of this study is to analyze the relationship between the dollar exchange rate and government bond interest rates for Turkiye. For this purpose, data is collected for the days when the last 10 policy rates published by the CBRT were announced. Data is obtained investing.com. Vector Autoregression (VAR) is used to measure the relationship in between two variables. The VAR system is based on empirical regularities embedded in the data. The VAR model may be viewed as a system of reduced form equations in which each of the endogenous variables is regressed on its own lagged values and the lagged values of all other variables in the system. Vector Autoregressive models are widely used in time series research to examine the dynamic relationships exist in between variables that interact with one another. In addition, VAR models are viable forecasting tools used often by macroeconomic or policy-making institutions. . In this study first, the stationary levels of the variables are determined by using Unit Root Test. Second, pre-tests of autocorrelation, heteroscedasticity and normality are conducted for the validity of the VAR model. Third, the short-term relationship between variables is tested by using VAR Granger Causality Test. Fourth, VAR analysis is utilized by applying Impulse-Response Analysis and Variance Decomposition Analysis . And finally, the long-term relationship between variables is tested by using Johansen Cointegration Test. Vector Autoregressionmodel is employed in this study. Findings- According to the results of Granger Causality test, government bond interest rates strongly affect the changes of exchange rate. However, there is no causality from exhange rates to interest rates. Therefore, the changes of interest rates are the main determinants of the changes of exchange rates in this short period. The results of Impulse-Response Test show that an unexpected shock (an unexpected increase) in government bond interest rates affects the exchange rates and increases it significantly. More, an unexpected increase in the exchange rates causes the interest rates on government bond to increase. The results of the variance decomposition test show that 50% of the change in the variance of the exchange rates in the first period is explained by changes in bond interest while 30% of the change in the variance of bond interest rates is explained by the changes in exchange rates. The results of Johansen cointegration test support that there is a stable long-term relationship between dollar exchange rates and government bond interest rates. Conclusion-This study focuses on the relationship between government bond interest rates and the dollar exchange rates in Turkiye for the last 10 policy interest rates announcements by Cenral Bank of Turkiye. In summary, the changes in interest rates on bonds affect the changes in exchange rates more. Data for the days that the CBRT issued the last ten policy rates is gathered for this purpose. The association between two variables is measured using Vector Autoregression (VAR). According to overall results, the changes in interest rates on bonds affect the changes in exchange rates more. Keywords: Policy rate, exchange rate, interest rate, Turkiye, Granger Causality, VAR model JEL Codes: E40, E50, C10, C58
Financial literacy is explained as the cognitive understanding of financial indicators and risk aversion, risk perception and investor behavior. Perhaps the investor behavior may vary depending on several factors such as gender, age, income level, social status, education etc. This research aims to highlight the effect of gender on financial market perception among Turkish investors. The outputs of two surveys the first for the last quarter of 2022 and the second for the first quarter of 2023, are analysed and compared. Therefore, two consecutive quarters are compared by gender for investment behaviors. This reserach observes some factors such as stress level, portfolio holding times, investment decisions and expectations regarding cryptocurrency markets. The methodology follows the Cronbach Alpha, Kolmogorov-Smirnov and ShapiroWilk Normality, and Mann-Whitney U tests respectively. The findings support gender differences in perception and investment behavior.
I am a PhD candidate in Business Administration and a financial econometrician working on empirical finance and macro-financial dynamics. I…