Research Article
Year 2019,
Volume: 17 Issue: 4, 59 - 71, 30.12.2019
Abstract
Çalışmada
2008 küresel finansal krizden sonra ortaya çıkan ancak halen tam olarak para
muamelesi görmeyen temel kripto paralar olan Bitcoin ve Ripple’ın getiri
oranlarının volatilite özellikleri modellenmiştir. Uygulamalı analizde Bitcoin
ve Ripple getiri oranları için geleneksel ARCH ve GARCH modelleri yanında
asimetriyi de dikkate alan EGARCH ve TGARCH modelleri de tahmin edilmiştir.
Alternatif modellerin öngörü performanslarına göre yapılan karşılaştırmada en
başarılı olan model olarak asimetriyi dikkate alan TGARCH modeli bulunmuştur.
Ayrıca en başarılı modelden elde edilen koşullu varyansların grafiği
incelendiğinde volatilitenin yükseldiği dönemlerin kripto paraların
fiyatlarında büyük oynaklıkların olduğu dönemlerle örtüştüğü gözlenmiştir.
Keywords
References
- Bouri, E., Geroges, A. ve Dyhrberg, A.H. (2017). On the Return-Volatility Relationship in The Bitcoin Market Around The Price Crash Of 2013, Economics: The Open-Access, Open-Assessment E-Journal, 11, 1–16.Bouoiyour, J. ve Selmi R. (2016). Bitcoin: A Beginning Of A New Phase?, Economics Bulletin, 36(3), 1430-1440.Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.Box, G. E. P. ve Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control, Holden Day, San Francisco.Dyhrberg, A. H (2016a). Hedging capabilities of Bitcoin. Is It the Virtual Gold?. Finance Research Letters, 16, 139-144.Dyhrberg, A. H. (2016b). Bitcoin, Gold and the Dollar: A GARCH Volatility Analysis, Finance Research Letters, 16, 85-92.Enders, W. (2004). Applied Econometric Time Series, New York: John Wiley and Sons.Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50(4), 987-1008. Ertuğrul, H.M. (2012). Türkiye’de Döviz Kuru Volatilitesi ve Enflasyon İlişkisi. Yayınlanmamış Doktora Tezi, Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.Ertuğrul, H. M ve Soytaş, U. (2013). Sanayi Üretim Endeksinin Durağanlık Özellikleri. İktisat, İşletme ve Finans, 328(28), 51-66. http://dx.doi.org/10.3848/ iif.2013.328.3751Gebeşoğlu, P.F. ve Ayhan F. (2019). Regulatory Aspects of Cryptocurrencies İçinde: Burak Darıcı ve Fatih AYHAN (Edt.), “Cryptocurrencies in all Aspects” (Sf.:41-59), Peter Lang International Academic Publishers, Bern: Switzerland. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M.C. ve Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users' Hidden Intentions, ECIS 2014 (Tel Aviv). Available at SSRN: https://ssrn.com/abstract=2425247.Glosten, L. R., Jagannathan, R. ve Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance, 48, 1779–1801.Gronwald, M. (2014). The Economics of Bitcoins- Market Characteristics and Price Jumps, CESifo Working Paper Series 5121, CESifo Group Munich.Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models, Economics Letters, 158, 3-6. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36, 394-419.Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Erişim Adresi: http://wfc-knowledgecentre.com/wp-content/uploads/2016/07/Bitcoin-A-Peer-to-Peer-electronic-Cash-System.pdfNelson, D. (1991). Conditional Heteroscedascity in Asset Returns: A New Approach, Econometrica, 59, 347-370.Ng, S. ve Peron P. (2001). Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. http:// dx.doi.org/10.1111/1468-0262.00256Zivot, E, ve Andrews, D. (1992). Further Evidence Of Great Crash, The Oil Price Shock And Unit Root Hypothesis, Journal of Business Economics and Statistics 10, 251-270.
Year 2019,
Volume: 17 Issue: 4, 59 - 71, 30.12.2019
Abstract
In this study, the volatility characteristics of the return rates of Bitcoin and Ripple, which emerged after the 2008 global financial crisis and are not yet fully accepted as currency, are modeled. In the empirical modeling, we employed both traditional ARCH/GARCH models and EGARCH-TGARCH models which take asymmetry into consideration. We compare alternative models according to their forecast performance and asymmetric TGARCH model is found as the most successful model according to forecast performance criteria. Also, when we examine conditional variance obtained from the most successful model, we observe that higher volatility periods overlap with the periods of high price movements of the analyzed crypto currencies.
References
- Bouri, E., Geroges, A. ve Dyhrberg, A.H. (2017). On the Return-Volatility Relationship in The Bitcoin Market Around The Price Crash Of 2013, Economics: The Open-Access, Open-Assessment E-Journal, 11, 1–16.Bouoiyour, J. ve Selmi R. (2016). Bitcoin: A Beginning Of A New Phase?, Economics Bulletin, 36(3), 1430-1440.Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.Box, G. E. P. ve Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control, Holden Day, San Francisco.Dyhrberg, A. H (2016a). Hedging capabilities of Bitcoin. Is It the Virtual Gold?. Finance Research Letters, 16, 139-144.Dyhrberg, A. H. (2016b). Bitcoin, Gold and the Dollar: A GARCH Volatility Analysis, Finance Research Letters, 16, 85-92.Enders, W. (2004). Applied Econometric Time Series, New York: John Wiley and Sons.Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50(4), 987-1008. Ertuğrul, H.M. (2012). Türkiye’de Döviz Kuru Volatilitesi ve Enflasyon İlişkisi. Yayınlanmamış Doktora Tezi, Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.Ertuğrul, H. M ve Soytaş, U. (2013). Sanayi Üretim Endeksinin Durağanlık Özellikleri. İktisat, İşletme ve Finans, 328(28), 51-66. http://dx.doi.org/10.3848/ iif.2013.328.3751Gebeşoğlu, P.F. ve Ayhan F. (2019). Regulatory Aspects of Cryptocurrencies İçinde: Burak Darıcı ve Fatih AYHAN (Edt.), “Cryptocurrencies in all Aspects” (Sf.:41-59), Peter Lang International Academic Publishers, Bern: Switzerland. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M.C. ve Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users' Hidden Intentions, ECIS 2014 (Tel Aviv). Available at SSRN: https://ssrn.com/abstract=2425247.Glosten, L. R., Jagannathan, R. ve Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance, 48, 1779–1801.Gronwald, M. (2014). The Economics of Bitcoins- Market Characteristics and Price Jumps, CESifo Working Paper Series 5121, CESifo Group Munich.Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models, Economics Letters, 158, 3-6. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36, 394-419.Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Erişim Adresi: http://wfc-knowledgecentre.com/wp-content/uploads/2016/07/Bitcoin-A-Peer-to-Peer-electronic-Cash-System.pdfNelson, D. (1991). Conditional Heteroscedascity in Asset Returns: A New Approach, Econometrica, 59, 347-370.Ng, S. ve Peron P. (2001). Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. http:// dx.doi.org/10.1111/1468-0262.00256Zivot, E, ve Andrews, D. (1992). Further Evidence Of Great Crash, The Oil Price Shock And Unit Root Hypothesis, Journal of Business Economics and Statistics 10, 251-270.
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Details
| Primary Language | Turkish |
|---|---|
| Subjects | Finance |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 30, 2019 |
| Published in Issue | Year 2019 Volume: 17 Issue: 4 |