Research Article
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Year 2019, Volume: 3 Issue: 2, 21 - 45, 31.05.2019
https://doi.org/10.25295/fsecon.2019.02.002

Abstract

References

  • Alpago, H. (2016). Bitcoin’den Selfcoin’e Kripto Para. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Ateş, B. A. (2016). Kripto Para Birimleri, Bitcoin ve Muhasebesi. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 7(1), 349-366.
  • Atik, M., Köse, Y., Yılmaz, B., & Sağlam, F. (2015). Kripto Para: Bitcoin ve Döviz Kurları Üzerine Etkileri. Bartın Üniversitesi İİBF Dergisi, 6(11), 247-262.
  • Baek, C., & 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., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Barski, C., & Wilmer, C. (2014). Bitcoin for the Befuddled.
  • Bilir, H., & Çay, Ş. (2016). Elektronik Para ve Finansal Piyasalar Arasındaki İlişki. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 21-31.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Brauneis, A. & R. Mestel (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58-61.
  • Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
  • Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge University Press, United Kingdom.
  • Brooks, R. D., Faff, R. W., McKenzie, M. D., & Mitchell, H. (2000). A multi-country study of power ARCH models and national stock market returns. Journal of International Money and Finance, 19(3), 377-397.
  • Cheah, E. & Fry, J. (2015). Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into The Fundamental Value of Bitcoin. Economics Letters, 130, 32-36.
  • Cheung, A., Roca, E., & Su, J. (2015). Crypto-Currency Bubbles: An Application of The Phillips–Shi–Yu (2013) Methodology on Mt. Gox Bitcoin Prices. Applied Economics, 47 (23), 2348-2358.
  • Christopher, C. M. (2014). Whack-a-Mole: Why Prosecuting Digital Currency Exchanges Won't Stop Online Laundering. Lewis & Clark Law Review, Forthcoming. http://ssrn.com/abstract=2312787.
  • Chu, J., Nadarajah, S., & Chan, S. (2015). Statistical Analysis of The Exchange Rate of Bitcoin. PloS one, 10 (7), 1-27.
  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-18.
  • Ding, Z., & Granger, C. W. (1996). Modeling volatility persistence of speculative returns: a new approach. Journal of Econometrics, 73(1), 185-215.
  • Dirican, C., & Canoz, I. (2017). The Cointegration Relationship Between Bitcoin Prices and Major World Stock Indices: An Analysis with ARDL Model Approach. Journal of Economics, Finance and Accounting, 4(4), 377-392.
  • Dizkırıcı, A. S., & Gökgöz, A. (2018). Kripto Para Birimleri ve Türkiye'de Bitcoin Muhasebesi. Journal of Accounting, Finance and Auditing Studies, 4(2), 92-105.
  • Dong, H., & Dong, W. (2014). Bitcoin: Exchange rate parity, risk premium, and arbitrage stickiness. British Journal of Economics, Management & Trade, 5(1), 105-113.
  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • Edwards, C. (2015). Finance-Bitcoin price crash finds new victims. Engineering & Technology, 10(2), 19-19.
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  • Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
  • Franco, P. (2014). Understanding Bitcoin: Cryptography, engineering and economics. John Wiley & Sons.
  • Frascaroli, B. F., & Pinto, T. C. (2016). The Innovative Aspects Of Bitcoin, Market Microstructure And Returns Volatility: An Approach Using Mgarch.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.
  • Grinberg, R. (2012). Bitcoin: An Innovative Alternative Digital Currency. Hastings Science & Technology Law Journal, 4, 159.
  • Gültekin, Y., & Bulut, Y. (2016). Bitcoin Ekonomisi: Bitcoin Eko-Sisteminden Doğan Yeni Sektörler ve Analizi. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(3), 82-92.
  • Halaburda, H. & M. Sarvary (2016). Beyond Bitcoin. The Economics of Digital Currencies.
  • Hencic, A. & C. Gouriéroux (2015). Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates, In Econometrics of Risk. Springer, Cham.
  • Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158, 3-6.
  • Kristoufek, L. (2013). BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific reports, 3, 3415.
  • Kristoufek, L. (2015). What are The Main Drivers of The Bitcoin Price? Evidence from Wavelet Coherence Analysis. PloS one, 10 (4).
  • MacDonell, A. (2014). Popping the Bitcoin bubble: An application of log-periodic power law modeling to digital currency. University of Notre Dame working paper.
  • Malhotra, A., & Maloo, M. (2014). Bitcoin–is it a Bubble? Evidence from Unit Root Tests.
  • Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • Pieters, G., & Vivanco, S. (2017). Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy, 39, 1-14.
  • Reid, F., & Harrigan, M. (2013). An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • Stavroyiannis, S. (2017). Value-at-Risk and Expected Shortfall for the major digital currencies. arXiv preprint arXiv:1708.09343.
  • Szmigielski, A. (2016). Bitcoin Essentials. Packt Publishing Ltd.
  • Tsay, R. S. (2010). Analysis of Financial Time Series. John Wiley & Sons, Third Edition.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • Urquhart, A. (2018). What causes the attention of Bitcoin?. Economics Letters, 166, 40-44.
  • Wegdell, A., & Andersson, G. (2014). Prospects of Bitcoin-An evaluation of its future.

The Volatility Structure of Cryptocurrencies: The Comparison of GARCH Models

Year 2019, Volume: 3 Issue: 2, 21 - 45, 31.05.2019
https://doi.org/10.25295/fsecon.2019.02.002

Abstract

Forecasting models based on the assumption that returns are normally distributed do not perform sufficiently on shallow markets. These models are more likely to fail in the estimation of the extreme points that can be reached especially at high volatility markets, and this situation is led to investors in predicting volatility. In the volatility forecasting of crypto money, which is seen as an alternative investment tool for the financial investors, single volatility models such as, ARCH, GARCH, T-GARCH, GARCH-M, E-GARCH, and I-GARCH and long memory models (AP-GARCH and C-GARCH) was utilized. In addition, the most suitable model was tried to be tested among the models used for volatility estimation. In this context, the price data of Bitcoin, Ethereum and Ripple cryptocurrency with the highest market value in the crypto money market have been utilized between 24/08/2016-07/05/2018. According to the results of the research, for Bitcoin and Ethereum, the volatility effect of the shocks is permanent and the effect of the positive shocks is more than that of the negative shocks, whereas for Ripple, the volatility effect of the shocks is transient and the passivity of the volatility is short.

References

  • Alpago, H. (2016). Bitcoin’den Selfcoin’e Kripto Para. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Ateş, B. A. (2016). Kripto Para Birimleri, Bitcoin ve Muhasebesi. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 7(1), 349-366.
  • Atik, M., Köse, Y., Yılmaz, B., & Sağlam, F. (2015). Kripto Para: Bitcoin ve Döviz Kurları Üzerine Etkileri. Bartın Üniversitesi İİBF Dergisi, 6(11), 247-262.
  • Baek, C., & 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., & Roubaud, D. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Barski, C., & Wilmer, C. (2014). Bitcoin for the Befuddled.
  • Bilir, H., & Çay, Ş. (2016). Elektronik Para ve Finansal Piyasalar Arasındaki İlişki. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 21-31.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Brauneis, A. & R. Mestel (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58-61.
  • Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
  • Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge University Press, United Kingdom.
  • Brooks, R. D., Faff, R. W., McKenzie, M. D., & Mitchell, H. (2000). A multi-country study of power ARCH models and national stock market returns. Journal of International Money and Finance, 19(3), 377-397.
  • Cheah, E. & Fry, J. (2015). Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into The Fundamental Value of Bitcoin. Economics Letters, 130, 32-36.
  • Cheung, A., Roca, E., & Su, J. (2015). Crypto-Currency Bubbles: An Application of The Phillips–Shi–Yu (2013) Methodology on Mt. Gox Bitcoin Prices. Applied Economics, 47 (23), 2348-2358.
  • Christopher, C. M. (2014). Whack-a-Mole: Why Prosecuting Digital Currency Exchanges Won't Stop Online Laundering. Lewis & Clark Law Review, Forthcoming. http://ssrn.com/abstract=2312787.
  • Chu, J., Nadarajah, S., & Chan, S. (2015). Statistical Analysis of The Exchange Rate of Bitcoin. PloS one, 10 (7), 1-27.
  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-18.
  • Ding, Z., & Granger, C. W. (1996). Modeling volatility persistence of speculative returns: a new approach. Journal of Econometrics, 73(1), 185-215.
  • Dirican, C., & Canoz, I. (2017). The Cointegration Relationship Between Bitcoin Prices and Major World Stock Indices: An Analysis with ARDL Model Approach. Journal of Economics, Finance and Accounting, 4(4), 377-392.
  • Dizkırıcı, A. S., & Gökgöz, A. (2018). Kripto Para Birimleri ve Türkiye'de Bitcoin Muhasebesi. Journal of Accounting, Finance and Auditing Studies, 4(2), 92-105.
  • Dong, H., & Dong, W. (2014). Bitcoin: Exchange rate parity, risk premium, and arbitrage stickiness. British Journal of Economics, Management & Trade, 5(1), 105-113.
  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  • Edwards, C. (2015). Finance-Bitcoin price crash finds new victims. Engineering & Technology, 10(2), 19-19.
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  • Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
  • Franco, P. (2014). Understanding Bitcoin: Cryptography, engineering and economics. John Wiley & Sons.
  • Frascaroli, B. F., & Pinto, T. C. (2016). The Innovative Aspects Of Bitcoin, Market Microstructure And Returns Volatility: An Approach Using Mgarch.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.
  • Grinberg, R. (2012). Bitcoin: An Innovative Alternative Digital Currency. Hastings Science & Technology Law Journal, 4, 159.
  • Gültekin, Y., & Bulut, Y. (2016). Bitcoin Ekonomisi: Bitcoin Eko-Sisteminden Doğan Yeni Sektörler ve Analizi. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(3), 82-92.
  • Halaburda, H. & M. Sarvary (2016). Beyond Bitcoin. The Economics of Digital Currencies.
  • Hencic, A. & C. Gouriéroux (2015). Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates, In Econometrics of Risk. Springer, Cham.
  • Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models. Economics Letters, 158, 3-6.
  • Kristoufek, L. (2013). BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era. Scientific reports, 3, 3415.
  • Kristoufek, L. (2015). What are The Main Drivers of The Bitcoin Price? Evidence from Wavelet Coherence Analysis. PloS one, 10 (4).
  • MacDonell, A. (2014). Popping the Bitcoin bubble: An application of log-periodic power law modeling to digital currency. University of Notre Dame working paper.
  • Malhotra, A., & Maloo, M. (2014). Bitcoin–is it a Bubble? Evidence from Unit Root Tests.
  • Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • Pieters, G., & Vivanco, S. (2017). Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy, 39, 1-14.
  • Reid, F., & Harrigan, M. (2013). An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • Stavroyiannis, S. (2017). Value-at-Risk and Expected Shortfall for the major digital currencies. arXiv preprint arXiv:1708.09343.
  • Szmigielski, A. (2016). Bitcoin Essentials. Packt Publishing Ltd.
  • Tsay, R. S. (2010). Analysis of Financial Time Series. John Wiley & Sons, Third Edition.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • Urquhart, A. (2018). What causes the attention of Bitcoin?. Economics Letters, 166, 40-44.
  • Wegdell, A., & Andersson, G. (2014). Prospects of Bitcoin-An evaluation of its future.
There are 48 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

İbrahim Korkmaz Kahraman 0000-0001-5083-3586

Habib Küçükşahin 0000-0003-2967-9814

Emin Çağlak 0000-0003-2798-7450

Publication Date May 31, 2019
Published in Issue Year 2019 Volume: 3 Issue: 2

Cite

APA Kahraman, İ. K., Küçükşahin, H., & Çağlak, E. (2019). The Volatility Structure of Cryptocurrencies: The Comparison of GARCH Models. Fiscaoeconomia, 3(2), 21-45. https://doi.org/10.25295/fsecon.2019.02.002

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