Analysis of Bitcoin Volatility during the COVID-19 Pandemic: An Examination Using ARCH and GARCH Models

Yıl 2024, Cilt: 9 Sayı: 4, 812 - 831, 31.12.2024

Ulaş Ünlü , Vildan Bayram

https://doi.org/10.30784/epfad.1588310

Öz

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.

Anahtar Kelimeler

Bitcoin, Volatility, GARCH, ARCH, COVID-19

Bitcoin Volatilitesinin COVID-19 Pandemisi Döneminde Analizi: ARCH ve GARCH Modelleriyle Bir İnceleme

Öz

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.

Anahtar Kelimeler

Bitcoin, GARCH, ARCH, Volatilite, COVID-19

Kaynakça

• 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
• 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
• Blockchain. (2024). Total circulating Bitcoin. Retrieved from https://www.blockchain.com/charts/total-bitcoins
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
• Brooks, C. (2003). Introductory econometrics for finance. The Economic Journal, 113(488), F397-F398. https://doi.org/10.1111/1468-0297.13911
• Brooks, C. (2008). Introductory econometrics for finance (2nd ed.). United Kingdom: Cambridge University Press. https://doi.org/10.1017/CBO9780511841644
• 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/
• 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
• 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
• 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
• Dilek, Ş. (2018). Blokchain teknolojisi ve Bitcoin (SETA Analiz, Sayı: 231). Erişim adresi: https://www.setav.org/assets/uploads/2018/02/231.-Bitcoin.pdf
• 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
• 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
• 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
• 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/
• IEA. (2020). An unprecedented global health and economic crisis. Retrieved from https://www.iea.org/topics/covid-19
• 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
• 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/
• 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
• 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
• Kirchgässner, G. and Wolters, J. (2007). Introduction to modern time series analysis. Heidelberg: Springer.
• 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/
• 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
• 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
• 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/
• 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
• 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.
• 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
• Saleh, F. (2019). Volatility and welfare in a crypto economy. Available at SSRN http://dx.doi.org/10.2139/ssrn.3235467
• Şenol, Z. (2020). COVID-19 krizi ve finansal piyasalar. N. Toğuç (Ed.), Para ve finans içinde (s. 75-124). Ankara: İKSAD Publishing House
• 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
• Tsay, R.S. (1986). Analysis of financial time series. New York, USA: John Wiley & Sons Ltd.
• Yermack, D. (2020). Corporate governance and blockchains. Review of Corporate Finance Studies, 9(2), 282-304. https://doi.org/10.1093/rof/rfw074
• 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