TY - JOUR T1 - Analysis of Bitcoin Volatility during the COVID-19 Pandemic: An Examination Using ARCH and GARCH Models TT - Bitcoin Volatilitesinin COVID-19 Pandemisi Döneminde Analizi: ARCH ve GARCH Modelleriyle Bir İnceleme AU - Ünlü, Ulaş AU - Bayram, Vildan PY - 2024 DA - December Y2 - 2024 DO - 10.30784/epfad.1588310 JF - Ekonomi Politika ve Finans Araştırmaları Dergisi JO - EPF Journal PB - Ekonomi ve Finansal Araştırmalar Derneği WT - DergiPark SN - 2587-151X SP - 812 EP - 831 VL - 9 IS - 4 LA - en AB - The COVID-19 pandemic has had a profound effect on the global economy and financial markets, including a significant impact on the cryptocurrency markets. This study analyzes the impact of the COVID-19 process on bitcoin price movements. The study examines the daily price data of bitcoin between 01/03/2020 and 01/04/2022 and uses ARCH and GARCH models to estimate volatility. The results show that there was a significant increase in bitcoin volatility during the initial period of the pandemic. This reflects a period when the pandemic increased uncertainty in financial markets and spurred investor interest in cryptocurrencies. While the ARCH model showed limited success in analyzing the short-term dynamics of volatility, the GARCH model captured the long-term trends in volatility more effectively. However, both models were insufficient to fully predict the sudden and extreme increases in volatility observed during crisis periods such as the pandemic. In addition to analyzing the impact of the pandemic on cryptocurrency markets, the study provides important implications for investor behavior and volatility management. In this context, it highlights the importance of developing risk management and regulatory frameworks in cryptocurrency markets. KW - Bitcoin KW - Volatility KW - GARCH KW - ARCH KW - COVID-19 N2 - COVID-19 pandemi süreci küresel ekonomi ve finansal piyasalar üzerinde derin etkiler bırakmış olup bu durum kripto para piyasalarını da önemli ölçüde etkilemiştir. Bu çalışmada COVID-19 sürecinin Bitcoin fiyat hareketleri üzerindeki etkileri analiz edilmiştir. Araştırmada, Bitcoin'in 01/03/2020- 01/04/2022 tarihleri arasındaki günlük fiyat verileri incelenmiş ve ARCH ve GARCH modelleri kullanılarak volatilite tahmini yapılmıştır. Bulgular pandeminin başlangıç döneminde Bitcoin’in volatilitesinde belirgin bir artış olduğunu göstermektedir. Pandemi dönemi finansal piyasalardaki belirsizlikleri artırmakla birlikte yatırımcıların kripto paraları ilgisinin de yükseldiği bir dönemi yansıtmaktadır. Çalışmada kullanılan ARCH modeli, volatilitenin kısa vadeli dinamiklerini analiz etmede sınırlı bir başarı gösterirken, GARCH modeli sonuçları volatilitenin uzun vadeli eğilimlerini daha etkili bir şekilde yakalamıştır. Bununla birlikte her iki model de pandemi gibi kriz dönemlerinde gözlemlenen ani ve ekstrem volatilite artışlarını tam anlamıyla öngörmekte yetersiz kalmıştır. Çalışma, yalnızca pandeminin kripto para piyasalarındaki etkilerini analiz etmekle kalmayıp, yatırımcı davranışları ve volatilite yönetimi konularında da önemli çıkarımlar sağlamaktadır. Aynı zamanda kripto para piyasalarında risk yönetimi ve düzenleyici çerçevelerin geliştirilmesinin önemine işaret etmektedir. CR - Baek, C. and Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34. https://doi.org/10.1080/13504851.2014.916379 CR - Bera, A.K. and Higgins, M.L. (1993). ARCH models: Properties, estimation and testing. Journal of Economic Surveys, 7(4), 305-366. https://doi.org/10.1111/j.1467-6419.1993.tb00170.x CR - Blockchain. (2024). Total circulating Bitcoin. Retrieved from https://www.blockchain.com/charts/total-bitcoins CR - Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 CR - Brooks, C. (2003). Introductory econometrics for finance. The Economic Journal, 113(488), F397-F398. https://doi.org/10.1111/1468-0297.13911 CR - Brooks, C. (2008). Introductory econometrics for finance (2nd ed.). United Kingdom: Cambridge University Press. https://doi.org/10.1017/CBO9780511841644 CR - Christopher, C.M. (2014). Whack-a-mole: Why prosecuting digital currency exchanges won't stop online laundering. Lewis & Clark Law Review, 18(1), 1-36. Retrieved from https://law.lclark.edu/law_reviews/lewis_and_clark_law_review/ CR - Chu, J. Nadarajah, S. and Chan, S. (2015). Statistical analysis of the exchange rate of Bitcoin. PloS One, 10(7), 1-27. https://doi.org/10.1371/journal.pone.0133678 CR - Ciaian, P. Rajcaniova, M. and Kancs, D.A. (2016). The economics of Bitcoin price formation. Applied Economics, 48(19), 1799-1815. https://doi.org/10.1080/00036846.2015.1109038 CR - Çetinkaya, Ş. (2018). Kripto paraların gelişimi ve para piyasalarındaki yerinin SWOT analizi ile incelenmesi. Uluslararası Ekonomi ve Siyaset Bilimleri Akademik Araştırmalar Dergisi, 2(5), 11-21. https://doi.org/10.23834/isrjournal.1199344 CR - Dilek, Ş. (2018). Blokchain teknolojisi ve Bitcoin (SETA Analiz, Sayı: 231). Erişim adresi: https://www.setav.org/assets/uploads/2018/02/231.-Bitcoin.pdf CR - Dyhrberg, A.H. (2016). Bitcoin, gold and the dollar - A GARCH volatility analysis. Finance Research Letters, 16, 85-92. https://doi.org/10.1016/j.frl.2015.10.008 CR - Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007. https://doi.org/10.2307/1912773 CR - Georgoula, I. Pournarakis, D. Bilanakos, C. Sotiropoulos, D. and Giaglis, G.M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices. Paper presented at the Mediterranean Conference on Information Systems (MCIS). Retrieved from http://aisel.aisnet.org/mcis2015/20 CR - Göktaş, P. Aksu, A. (2021). Endüstri 4.0 ile beraber blok zincir (blockchain) teknolojisi, bitcoin ve sanal paraların gelecekteki olası etkileri. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(3), 279-293. Erişim adresi: https://dergipark.org.tr/en/pub/sduiibfd/ CR - IEA. (2020). An unprecedented global health and economic crisis. Retrieved from https://www.iea.org/topics/covid-19 CR - Kahraman, İ.K. Küçükşahin, H. and Çağlak, E. (2019). Kripto para birimlerinin volatilite yapısı: GARCH modelleri karşılaştırması. Fiscaoeconomia, 3(2), 21-45. https://doi.org/10.25295/fsecon.2019.02.002 CR - Karpuz, E. ve Koç, Y.D. (2022). Covid-19 pandemisinin finansal piyasalar üzerindeki etkisi: Borsa İstanbul örneği. Uluslararası Afro-Avrasya Araştırmaları Dergisi, 7(14), 77-89. Erişim adresi: https://dergipark.org.tr/tr/pub/ijar/ CR - Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6. https://doi.org/10.1016/j.econlet.2017.06.023 CR - Kazan, H. (2020). COVID-19’un pay piyasası ve işletmeler üzerindeki etkisi. COVID-19 pandemisinin ekonomik, toplumsal ve siyasal etkileri içinde (s. 263–293). İstanbul: Istanbul University Press. doi: 10.26650/B/SS46.2020.005.17 CR - Kirchgässner, G. and Wolters, J. (2007). Introduction to modern time series analysis. Heidelberg: Springer. CR - Kristoufek, L. (2013). Bitcoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports, 3, 3415. Retrieved from https://www.nature.com/ CR - MacDonell, A. (2014). Popping the Bitcoin bubble: An application of log-periodic power law modeling to digital currency (University of Notre Dame working paper). Retrieved from https://economics.nd.edu/assets/134206/mac_donell_popping_the_biticoin_bubble_an_application_of_log_periodic_power_law_modeling_to_digital_currency.pdf CR - Marisetty, N. (2024). Applications of GARCH models in forecasting financial market volatility: Insights from leading global stock indexes. Asian Journal of Economics, Business and Accounting, 24(9), 63-84. https://doi.org/10.9734/ajeba/2024/v24i91477 CR - Ngunyi, A. Mundia, S. and Omari, C. (2019). Modelling volatility dynamics of cryptocurrencies using GARCH models. Journal of Mathematical Finance, 9(4), 591-615. Retrieved from https://www.scirp.org/journal/jmf/ CR - Ni, X., Wang, Z., Akbar, A. and Ali, S. (2022). Measuring natural resources rents volatility: Evidence from EGARCH and TGARCH for global data. Resources Policy, 76, 102553. https://doi.org/10.1016/j.resourpol.2022.102553 CR - Öztürk, A. and Dilek, Ö. (2021). The effects of covid-19 process on consumer digital service usage. In A.N. Özker (Ed.), Atlas International Congress on Social Sciences 8 abstract book (pp. 3-4). Papers presented at the Atlas International Congress on Social Sciences, Ankara, Türkiye. Adıyaman: IKSAD Global Publication. CR - Pieters, G. and Vivanco, S. (2017). Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy, 39, 1-14. https://doi.org/10.1016/j.infoecopol.2017.02.002 CR - Saleh, F. (2019). Volatility and welfare in a crypto economy. Available at SSRN http://dx.doi.org/10.2139/ssrn.3235467 CR - Şenol, Z. (2020). COVID-19 krizi ve finansal piyasalar. N. Toğuç (Ed.), Para ve finans içinde (s. 75-124). Ankara: İKSAD Publishing House CR - Stavroyiannis, S. (2017). Value-at-risk and expected shortfall for the major digital currencies. arXiv, 1708.09343. https://doi.org/10.48550/arXiv.1708.09343 CR - Tsay, R.S. (1986). Analysis of financial time series. New York, USA: John Wiley & Sons Ltd. CR - Yermack, D. (2020). Corporate governance and blockchains. Review of Corporate Finance Studies, 9(2), 282-304. https://doi.org/10.1093/rof/rfw074 CR - Yıldırım, H. and Bekun, F.V. (2023). Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models. Future Business Journal, 9(1), 75. https://doi.org/10.1186/s43093-023-00255-8 UR - https://doi.org/10.30784/epfad.1588310 L1 - https://dergipark.org.tr/tr/download/article-file/4380054 ER -