Kripto Para Piyasalarında Asimetrik Volatilitenin Tahmininde Doğru Koşullu Varyans Modelleri

Yıl 2024, Cilt: 39 Sayı: 4, 909 - 930

Onur Çelebi , Erhan Demireli

https://doi.org/10.24988/ije.1434189

Öz

Bu çalışma, 2023 yılı Eylül ayında işlem gören en yüksek hacimli 5 kripto para için karmaşık ve detaylı volatilite analizi yapmak üzerine Genelleştirilmiş Otoregresif Koşullu Heteroskedastiklik (GARCH) modeli ve türevleri üzerine testler içermektedir. Analizler, Python, R ve Eviews programlarıyla yapılarak sonuçların tutarlılığı test edilmiş ve doğrulanmıştır. Test süreçlerinde, kripto para piyasalarında volatilite tahmini açısından en doğru yöntemin hangisi olabileceği, çarpıklık, basıklık ve log-likelihood değerleri dikkate alınarak sınanmış ve model doğruluğu için Jarque-Bera, ADF gibi testler uygulanmıştır. Yapılan sınanmalar sonucunda volatilite analizinde kripto piyasalar için GARCH modellerinde EGARCH, GJR-GARCH ve TGARCH modellerinin ilgili kripto para birimlerinde volatilite ve piyasa şoklarını tespit etmede etkin olduğu bulunmuştur.

Anahtar Kelimeler

Kripto paralar, Koşullu Varyans, Asimetrik GARCH Modelleri, GARCH, E-GARCH, GJR-GARCH, T-GARCH

Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets

Öz

This study includes tests on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its derivatives to conduct complex and detailed volatility analysis for the 5 highest-volume cryptocurrencies traded in September 2023. The tests have been conducted with Python, R, and Eviews software and analyses have been compared in terms of consistency and accuracy of the results across multiple software and programming languagse. In the testing process, observation of the volatility has been assessed by some variables such as skewness, kurtosis, and log-likelihood values, and these variables have been taken into consideration for testing. Tests such as Jarque-Bera and Augmented Dickey-Fuller (ADF) have been applied during the process to verify model correctness. The EGARCH, GJR-GARCH, and TGARCH models have been more effective in detecting volatility and market shocks in the relevant cryptocurrencies as a result of the tests conducted in the volatility analysis.

Anahtar Kelimeler

Cryptocurrencies, Conditional Variance, Asymmetric GARCH Models, GARCH, E-GARCH, GJR-GARCH, T-GARCH

Kaynakça

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