Bitcoin İçin Volatilite Tahmini: Simetrik ve Asimetrik Garch Modelleri İçin Ampirik Bir Uygulama

Amaç - Bu araştırmanın amacı, kripto para piyasasında en büyük kapitalizasyona ve en çok işlem hacmine sahip kripto para olan Bitcoin’in, volatilitesini en iyi açıklayan modelin incelenmesidir. Yöntem – Araştırmada; GARCH (Doğrusal Olmayan Genelleştirilmiş Otoregresif Koşullu Heteroskedastisite) sınıfı olan; GARCH, EGARCH, IGARCH, GJR, FIGARCH-BBM, FIGARCH-CHUNG, FIEGARCH, FIAPARCH-BBM, FIAPARCH-CHUNG ve HYGARCH modelleri kullanılmıştır. Araştırmada Bitcoin’in, 30.04.2013 ile 26.02.2021 dönemindeki 2860 günlük ABD Doları (USD) cinsinden günlük kapanış fiyat değerleri, veri seti olarak kullanılmıştır. Bulgular - Araştırma sonucuna göre Bitcoin için en uygun tahminler, HYGARCH modeli ile yapılmaktadır. Ayrıca örneklem dışı oynaklığı en iyi öngören ve en yüksek performansa sahip olan GARCH modeli; 1 günlük öngörü için FIAPARCH-BBM modeli, 5 ve 10 günlük öngörü için ise FIGARCH-CHUNG modelidir. Tartışma - Bitcoin fiyat oynaklığı, Bitcoin opsiyonlarını fiyatlandırma formüllerinde, portföy seçiminde ve risk ölçümünde önemli bir girdidir. Bu nedenle, Bitcoin fiyatlarındaki dalgalanmanın modellenmesi ve tahmin edilmesi, hem bireysel ve kurumsal yatırımcılar için hem de teorisyenler için büyük bir öneme sahiptir. Bu nedenle araştırma sonuçlarının, bireysel ve kurumsal yatırımcıların portföy riski tahminlerinde faydalı olacağı düşünülmektedir. Gelecekteki araştırmalarda, farklı kripto paralar için farklı volatilite tahmin modellerin performanslarının incelenmesi ile bireysel ve kurumsal yatırımcılara yeni ve faydalı bilgiler sunulabileceği de düşünülmektedir.

Volatility Forecast For Bitcoin: An Empirical Application for Symmetric And Asymmetric Garch Models

Purpose - The aim of this research is to examine the model that best explains the volatility of Bitcoin, which has the largest capitalization and the largest transaction volume in the cryptocurrency market. Design/methodology/approach – In research; GARCH (Nonlinear Generalized Autoregressive Conditional Heteroscedasticity) class; GARCH, EGARCH, IGARCH, GJR, FIGARCH-BBM, FIGARCHCHUNG, FIEGARCH, FIAPARCH-BBM, FIAPARCH-CHUNG and HYGARCH models were used. In the research, the daily closing price values of Bitcoin in 2860 days in US Dollars (USD) between 30.04.2013 and 26.02.2021 were used as a data set. Findings - According to the results of the research, the most appropriate predictions for Bitcoin are made with the HYGARCH model. In addition, the GARCH model, which best predicts out-of-sample volatility and has the highest performance; FIAPARCH-BBM model for 1-day forecast, FIGARCH-CHUNG model for 5- and 10-day forecast. Discussion - Bitcoin price volatility is an important input in Bitcoin options pricing formulas, portfolio selection, and risk measurement. Therefore, modeling and predicting Bitcoin price fluctuation is of great importance for both individual and institutional investors and theorists. For this reason, it is thought that the results of the research will be useful in the portfolio risk estimations of individual and institutional investors. In future research, it is thought that new and useful information can be presented to individual and institutional investors by examining the performance of different volatility prediction models for different cryptocurrencies.

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