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Yayın Backcasting Bitcoin volatility: ARCH and GARCH approaches(Suat Teker, 2024-12-31) Teker, Dilek; Teker, Suat; Demirel Gümüştepe, EsinPurpose- 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.Yayın Investor behavior and risk perception: a gender perspective(International Strategic Management and Managers Association, 2023-12) Teker, Dilek; Teker, Suat; Demirel Gümüştepe, EsinFinancial 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.Yayın Measurement of market risk premiums: a sectoral analysis on Borsa Istanbul(Institute for Promoting Research & Policy Development (IPRPD), 2023-12) Teker, Dilek; Teker, Suat; Demirel Gümüştepe, EsinThe determination of market risk premium is often a problem for academics and practioners when applying Capital Asset Pricing Model (CAPM). This study intends to measure the market risk premiums (MRP) in the sectors of Borsa Istanbul. The monthly data are extracted from Reuters Database for the period of 2016-2021 for the seventeen sectors of Borsa Istanbul. The whole sampling period is devided into two sub-periods based upon the results of Cusum-Squared test statistics showing a structural break with the Covid-19. The emprical results reveal that the market risk premium on BIST100 is -0.7% with a volatility of 0.3% in the pre-Covid-19 period while the market risk premium on BIST100 is -0.21% with a volatlity of 0.23% post-Covid-19 period. The findings show that a significant increase in market risk premiums and volatilies post-Covid-19 era compared to the pre-Covid era. More, the market risk premiums and their volatilies are estimated by utilizing ARIMA model for the 2022-2024 period. The estimates point even higher market risk premiums and volatilies in the near future.Yayın Backcasting Bitcoin prices: implementation with ARCH & GARCH models(International Journal of Economics, Commerce and Management, 2024-12) Teker, Dilek; Teker, Suat; Demirel Gümüştepe, EsinBitcoin, the first decentralized cryptocurrency, has gained popularity among investors for several reasons. Its potential for high returns makes it attractive to those seeking alternatives to traditional investments. Bitcoin's volatility provides both risk and reward, drawing in speculative investors. Moreover, Bitcoin operates independently of central banks or governments, appealing to those wary of inflation and economic instability. As more businesses and financial institutions adopt Bitcoin as an investment tool and a medium of exchange, its appeal continues to grow. For institutional investors, Bitcoin offers a way to diversify portfolios amid low interest rates and geopolitical uncertainty. However, the volatility in Bitcoin markets tends to be a risk exposure, so developing models to understand Bitcoin fluctuations is crucial to determining more about market behavior. Accurate financial models help predict price movements, manage risk, and identify macroeconomic correlations. Given its complexity, these models are essential for long-term investors to navigate volatility and optimize their investment strategies. This research employs ARCH and GARCH models to forecast Bitcoin volatility. The outputs indicate that ARIMA is the best fit model that explains Bitcoin’s price fluctuations in the selected data period.
