Modeling the Volatility of Bitcoin Returns Using Egarch Method

The development process in financial markets give rise to the emergence of various financial instruments and cryptocurrencies, which are the newest tools of this process, are trying to integrate into the system. Even though the use of crypto-currencies forinvestment and speculation has increased, limited information on the market leads to high level of volatility in price and return. Therefore, this study aims to analyze the volatility dynamics of the returns of Bitcoin, which is the cryptocurrency with the largest market volume, using the weekly data set for 2013:04-2020:09period. In this context, Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model is employed to investigate the asymmetric volatility, which refers to the asymmetric effects of positive and negative shocks. The results of the analysis show that the leverage effect applies to Bitcoin returns. In other words, the asymmetric effect between good and bad news is revealed. Moreover, the fact that the parameter of the volatility resistance has a high value reflects that the asymmetric past period shocks have a significant effect on the current period conditional variance.

Bitcoin GetiriVolatilitesinin Egarch Yöntemi İle Modellenmesi

Finansal piyasalardaki gelişim süreci çeşitli finansal araçların ortaya çıkmasına neden olmakta ve bu sürecin en yeni aracı olan kripto paralar ise sisteme entegre olmaya çalışmaktadırlar. Kripto paraların yatırım ve spekülasyon amacıyla artan kullanımı her ne kadar ivme kazansa da piyasa hakkında oldukça az bilgiye sahip olunması fiyat ve getiri dalgalanmalarının yüksek hızda seyretmesine yol açmaktadır. Dolayısıyla bu çalışma, en büyük piyasa hacmine sahip kripto para olan Bitcoin getirilerinin volatilitedinamiklerini 2013:04-2020:09dönemine ilişkin haftalık veri setini kullanarak incelemeyi amaçlamaktadır. Bu kapsamda, asimetrik oynaklığı, bir diğer ifadeyle pozitif ve negatif şokların asimetrik etkilerini araştırabilmek için Üstel Genelleştirilmiş Otoregresif Şartlı Değişen Varyans (EGARCH) Modeli kullanılmıştır. Analiz sonuçları, Bitcoin getirilerinde kaldıraç etkisinin geçerli olduğunu, bir diğer ifadeyle iyi ve kötü haberler arasındaki asimetrik etkinin kendini gösterdiğini ortaya koymuştur.Dahası, oynaklık direncine ait parametrenin oldukça yüksek değer alması, asimetrik geçmiş dönem şoklarının cari dönem şartlı varyansı üzerinde anlamlı bir etkisinin olduğunu yansıtmıştır.

Kaynakça

Alpago, H. (2018). “Bitcoin’den Selfcoin’e Kripto Para”, Uluslararası Bilimsel AraĢtırmalar Dergisi, 3(2), 411- 428.

Anavatan, A. and Kayacan, E.Y. (2019). “Are Bitcoin Returns Predictable”, Journal of Current Researches on Business and Economics,9(1), 13-22.

Baek, C. and Elbeck, M. (2015). “Bitcoins as an Investment or Speculative Vehicle? A First Look”, Applied Economics Letters, 22(1), 30-34.

Balcilar, M., Bouri, E., Gupta, R. and Rounbaud, D. (2017). “Can Volume Predict Bitcoin Returns and Volatility A Quantiles-Based Approach”, Economic Modelling, 64,74-81.

Bariviera, A. F. (2017). “The Ġnefficiency of Bitcoin Revisited: A Dynamic Approach”, Economics Letters, 161, 1-4.

Baur, D.G., Hong, K.J. and Lee, A.D. (2016). “Bitcoin: Currency or Asset?”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2736020.

Baur, D. G. and Dimpfl, T. (2018). “Excess Volatility as an Impediment for a Digital Currency”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2949754.

Blau, B. M. (2017). “Price Dynamics and Speculative Trading in Bitcoin”, Research in International Business and Finance, 41, 493-499.

Bouri, E., Gil-Alana, L.A., Gupta, R. and Roubaud, D. (2017). “Modelling Long Memory Volatility in The Bitcoin Market: Evidence of Persistence and Structural Breaks”, International Journal Finance and Economics, 24(1), 412-426.

Brauneis, A., and Mestel, R. (2018). “Price Discovery of Cryptocurrencies: Bitcoin and Beyond”, Economics Letters, 165, 58-61.

Briere, M., Oosterlinck, K. and Szafarz, A. (2015). “Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoin”, Journal of Asset Management, 16: 365–73.

Catania, L. and Sandholdt, M. (2019). “Bitcoin at High Frequency”, Journal of Risk and Financial Management, 12(36), 1-19.

Cheah, E.T., Tapas, M., Parhi, M. and Zhang, Z. (2018). “Long Memory Ġnterdependency and Ġnefficiency in Bitcoin Markets”. Economics Letters, 167: 18–25.

Chengyuan, Q. (2017). “BitCoin in China: Price Discovery and Volatility Transmission”, SSRN Elecronic Journal, 1–13, https://ssrn.com/abstract=2934031.

Cheung, A., Roca, E. And Su, J. (2015), “Crypto-Currency Bubbles: An Application of the Phillips–Shi–Yu Methodology on Mt. Gox Bitcoin Prices”, Applied Economics, 47(23), 2348–2358.

Chu, J., Chan, S., Nadarajah, S. and Osterrieder, J. (2017). “GARCH Modelling of Cryptocurrencies”, J. Risk Financial Management,10(17), 1-15.

Ciaian, P., Rajcaniova, M. and Kancs, D.A. (2016). “The Economics of Bitcoin Price Formation”, Applied Economıcs, 48(19), 1799-1815.

Doğan, H. (2018). “Ġslam Hukuku Açısından Kripto Paralar ve Blockchain ġifreleme Teknolojisi”, Selçuk Üniversitesi Hukuk Fakültesi Dergisi, 26(2), 225-253.

Dwyer, G.P. (2015). “The economics of Bitcoin and similar private digital currencies”, Journal of Financial Stability, 17, 81–91.

Dong, H. and Dong, W. (2014). “Bitcoin: Exchange Rate Parity, Risk Premium, and Arbitrage Stickiness”, British Journal of Economics, Management & Trade, 5(1).

Dyhrberg, A.H. (2016). “Bitcoin, Gold and The Dollar-A GARCH Volatility Analysis”, Finance Research Letters, 16, 85-92.

Enders, W. (2015). Applied Econometric Time Series. 4th Ed. The USA: John Wiley & Sons.

Fanusie, Y.J. and Robinson, T. (2018). “Bitcoin Laundering: An Analysis of Illicit Flows into Digital Currency Services”, Center on Sanctions and Illicit Finance, Foundation for Defense of Democracies.

Frascaroli, B. F. and Pinto, T. C. (2016). “The Innovative Aspects Of Bitcoin, Market Microstructure And Returns Volatility: An Approach Using Mgarch”, http://www.ufjf.br/encontroeconomiaaplicada/files/2016/05/artigo64MicroeconomiaAplicada.pdf.

Gandal, N. and Halaburda, H. (2014). “Competition in the Cryptocurrency Market”, Bank of Canada Working Paper 2014-33, 1-32.

Gandal, N., Hamrick, J.T, Moore, T. and Oberman, T. (2018). “Price Manipulation in the Bitcoin Ecosystem”, Journal of Monetary Economics, 95: 86–96.

Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. N. and Giaglis, G. M. (2015). “Using Time-Series nd Sentiment Analysis oo Detect the Determinants of Bitcoin Prices”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2607167.

Harvey, C. and Tepper, T. (2015). “Can You Really Beat The Market?”, Money, 44(2), 76-79.

Hencic, A. and Gouriéroux, C. (2015). “Noncausal Autoregressive Model in Application to Bitcoin/Usd Exchange Rates.”, In Econometrics of Risk (pp. 17-40). Springer International Publishing.

Hultman, H. (2018). “An Empirical Study on Bitcoin Using Garch and Stochastic Volatility Models”, Lund University Department of Economics, https://lup.lub.lu.se/student-papers/search/publication/8958504.

Ji, Q., Bouri, E., Gupta, R. and Roubaud, D. (2018). “Network Causality Structures among Bitcoin and Other Financial Assets: A Directed Acyclic Graph Approach”, The Quarterly Review of Economics and Finance, 70: 203–13.

Kasper, J. (2017). “Evolution of Bitcoin: Volatility Comparisons with Least Developed Countries Currencies”, SSRN Elecronic Journal, 1–22, https://ssrn.com/abstract=3052207.

Katsiampa, P., (2018). “An Empirical Investigation of Volatility Dynamics in the Cryptocurrency Market”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3202317.

Katsiampa, P., Corbet, S. and Lucey, B., (2019). “Volatility Spillover Effects in Leading Cryptocurrencies: A BEKK-MGARCH Analysis”, Finance Reseacrh Letters, 29(1), 68-74.

Korap, L. (2010). “An Econometric Essay for the Asymmetric Volatility Content of the Portfolio Flows: EGARCH Evidence from the Turkish Economy”. Sosyal Bilimler Dergisi, 4, 103-109.

Koutmos, D. (2018). “Bitcoin Returns and Transaction Activity”, Economics Letters, 167, 81-85.

Li, X. and Wang, C.A. (2017). “The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The case of Bitcoin”, Decision Support Systems, 95: 49–60.

MacDonell, A. (2014). “Popping the Bitcoin Bubble: An Application of Log-Periodic Power Law Modeling to Digital Currency.”, University of Notre Dame working paper, https://economics.nd.edu/assets/134206/mac_donell_popping_the_biticoin_bubble_an_application_of_l og_periodic_power_law_modeling_to_digital_currency.pdf.

Nakamoto, S. (2009). “Bitcoin: A Peer-to-Peer Electronic Cash System”, https://bitcoin.org/bitcoin.pdf.

Nelson, D. B. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, 59, 347-370.

Özden, Ü. H. (2008). “ĠMKB BileĢik 100 Endeksi Getiri Volatilitesinin Analizi”, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 7(13), 339-350.

Shi, S. (2018). “The Impact of Futures Trading on Intraday Spot Volatility and Liquidity: Evidence from Bitcoin Market”. SSRN Elecronic Journal, 1–14, https://ssrn.com/abstract=3094647.

Songül, H. (2010). Otoregresif Koşullu Değişen Varyans Modelleri: Döviz Kurları Üzerine Uygulama, Türkiye Cumhuriyet Merkez Bankası Uzmanlık Yeterlilik Tezi, Ankara.

Urquhart, A. (2018). “What Causes the Attention of Bitcoin”, SSRN Elecronic Journal, 1–12, https://ssrn.com/abstract=3097153.

Yermack, D., 2013. “Is Bitcoin a Real Currency? An Economic Appraisal”, SSRN Elecronic Journal, 1-23. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2361599.

Yi, S.Z.X. and Wang, G.J. (2018). “Volatility Connectedness in the Cryptocurrency Market: Is Bitcoin A Dominant Cryptocurrency”, International Review of Financial Analysis 60: 98–114.

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