Cryptocurrency Price Forecasting with GARCH Model and LS - SVM Regression: An Application on Bitcoin Price

Bu çalışmada amaç Bitcoin gibi kripto paralar için başarılı öngörülerin farklı yöntemlerle elde edilip edilemeyeceğinin belirlenmesidir. Çalışmada Bitcoin fiyatlarının (Bitcoin/$) kullanılma nedeni bu kripto para biriminin hala piyasada en yaygın kullanılan kripto para birimi olması ve kripto para birimleri piyasasının genel durumunu başarılı bir şekilde temsil edeceği düşüncesidir. Finansal piyasalara ait seriler spekülasyonlar gibi bazı nedenlerle dalgalanmalar içerebilmektedir. Ayrıca genellikle doğrusal olmayan değişimler içermektedir. Bu gibi özellikleri, finansal zaman serileri için öngörülerin elde edilmesinde başarısızlıklara yol açmaktadır. Bu çalışmanda klasik zaman serisi modellerinden GARCH modeli ve bir makine öğrenme yöntemi olan DVM – EKK yöntemiyle Bitcoin fiyat serisine ait kestirimler elde edilmiş, model performansları karşılaştırılmıştır. Çalışmada 01 Ocak 2017 – 29 Şubat 2020 dönemi, 1155 günlük Bitcoin fiyat serisi ( ) kullanılmıştır. Her iki modelde de Bitcoin fiyat serisi ve bu seriye ait oynaklıklar kullanılmış, dışsal değişkenler modellere dâhil edilmemiştir. Her iki modele göre de öngörüler 1 ay, 2 ay ve 3 aylık periyotlar için elde edilmiştir. GARCH ve DVM – EKK modelleri için MAPE oranlarına göre örneklem dışı başarılı öngörü oranları sırasıyla 1 ay için %98,0347 – %95,3423; 2 ay için %97,9544 – %96,1307 ve 3 ay için %98,1272 – %91,4874’dir. GARCH modeli her üç periyot için de daha başarılı sonuçlar elde edilmesini sağlamıştır. Çalışmanın bulgusu GARCH modelinin kripto para fiyat serisi için öngörülerin elde edilmesinde kullanılabileceği yönündedir.

GARCH Modeli ve DVM – EKK Regresyonu ile Kripto Para Fiyat Öngörüsü: Bitcoin Fiyatı Üzerine Bir Uygulama

The aim of this study is to determine whether successful predictions for cryptocurrencies such as Bitcoin can be obtained with different methods. The reason why Bitcoin prices (Bitcoin / $) are used in the study is that this cryptocurrency is still the most widely used cryptocurrency in the market, and the idea that it will successfully represent the overall state of the cryptocurrencies market. Financial market series may contain fluctuations for some reason, such as speculations. It also usually includes nonlinear changes. Such features lead to failures in obtaining forecasts for financial time series. In this study, with the GARCH model, one of the classicial time series models and LS - SVM method, a machine learning method, predictions of the Bitcoin price series were obtained, and model performances were compared. In the study, between January 01, 2017 and February 29, 2020, 1155 daily Bitcoin price series ( ) was used. In both models, the Bitcoin price series and the volatilities of this series were used, and external variables were not included in the models. For both models, forecasts were obtained for periods of 1 month, 2 months and 3 months. For GARCH and LS - SVM models, out of sample successful forecasting rates according to MAPE ratios were 98,0347% - 95,3423% for 1 month; 97,9544% - 96,1307% for 2 months and 98,1272% - 91,4874% for 3 months, respectively. The GARCH model has provided more successful results for all three periods. The finding of the study is that the GARCH model can be used to obtain forecasts for the crypto price series.

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Aalborg, H. A., Molnár, P., de Vries, J. E. (2019). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29, 255–265. https://doi.org/10.1016/J.FRL.2018.08.010
Adcock, R., Gradojevic, N. (2019). Non-fundamental, non-parametric Bitcoin forecasting. Physica A: Statistical Mechanics and Its Applications, 531, 121-727. https://doi.org/10.1016/J.PHYSA.2019.121727
Aslan, F., Pençe, I., Çeşmeli, M. S., Kalkan, A. (2018). Bitcoin’in Türkiye Piyasasındaki Değerinin Yapay Zeka Teknikleri ile Tahmini. In 5th International Management Information Systems Conference. 59–62
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. https://doi.org/10.1016/J.ECONMOD.2017.03.019
Balli, F., de Bruin, A., Chowdhury, M. I. H., Naeem, M. A. (2019). Connectedness of cryptocurrencies and prevailing uncertainties. Applied Economics Letters, 0(0), 1–7. https://doi.org/10.1080/13504851.2019.1678724
Bleher, J., Dimpfl, T. (2019). Today I got a million, tomorrow, I don’t know: On the predictability of cryptocurrencies by means of Google search volume. International Review of Financial Analysis, 63, 147–159. https://doi.org/10.1016/J.IRFA.2019.03.003
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327. https://doi.org/10.1109/TNN.2007.902962
Ceylan, F., Tüzün, R., Ekinci, O., Kahyaoğlu, H. (2018). Kripto Para Piyasasında Balonların Tespiti: Bıtcoın ve Ethereum Örneği. Business & Management Studies: An Internatıonal Journal, 6(3), 263–274. https://doi.org/10.15295/bmij.v6i3.355
Çılgın, C., Ünal, C., Alıcı, S., Akkol, E., Gökşen, Y. (2020). Metin Sınıflandırmada Yapay Sinir Ağları ile Bitcoin Fiyatları ve Sosyal Medyadaki Beklentilerin Analizi. MAKÜ-Uyg. Bil. Derg, 4(1), 106–126. https://doi.org/10.31200/makuubd.651904
CoinMarketCap. (2020). https://coinmarketcap.com/
Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/bf00994018
da Silva Filho, A. C., Maganini, N. D., de Almeida, E. F. (2018). Multifractal analysis of Bitcoin market. Physica A: Statistical Mechanics and Its Applications, 512, 954–967. https://doi.org/10.1016/J.PHYSA.2018.08.076
Demir, A., Akılotu, B., Kadiroğlu, Z., Şengür, A. (2019). Makine Öğrenmesi Yöntemleri Kullanılarak Bitcoin Tahmini. 2019 1st International Informatics and Software Engineering Conference, 1–4.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
Evci, S. (2020). Bitcoin Piyasasında Haftanın Günü Anomalisi. Alanya Akademik Bakış, 4(1), 53–61. https://doi.org/10.29023/alanyaakademik.664776
Fan, G.-F., Peng, L.-L., Hong, W.-C., Sun, F. (2016). Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173, 958-970. https://doi.org/10.1016/j.neucom.2015.08.051
Figá-Talamanca, G., Patacca, M. (2019). Does market attention affect Bitcoin returns and volatility? Decisions in Economics and Finance, 42(1), 135–155. https://doi.org/10.1007/s10203-019-00258-7
Garman, M. B., Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53(1), 67–78. http://www.jstor.org/stable/2352358
Giudici, P., Pagnottoni, P. (2019). High Frequency Price Change Spillovers in Bitcoin Markets. RISKS, 7(4). https://doi.org/10.3390/risks7040111
Güleç, Ö. F., Çevik, E., Bahadır, N. (2018). Bitcoin ile Finansal Göstergeler Arasındaki İlişkinin İncelenmesi. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(2), 18–37. https://doi.org/10.33707/akuiibfd.567902
Hattori, T. (2020). A forecast comparison of volatility models using realized volatility: evidence from the Bitcoin market. Applied Economics Letters, 27(7), 591–595. https://doi.org/10.1080/13504851.2019.1644421
Haykin, S. (1999). Neural networks: a comprehensive foundation (Second Edition ed.). Singapore: Prentice Hall PTR.
Isah, K. O., Raheem, I. D. (2019). The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market. Physica A: Statistical Mechanics and Its Applications, 536, 121032. https://doi.org/10.1016/J.PHYSA.2019.04.268
Kanat, E., Öget, E. (2018). Bitcoin ile Türkı̇ye ve G7 Ülke Borsaları Arasindaki Uzun ve Kısa Dönem İlı̇şkı̇lerin İncelenmesı̇. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 3(3), 601–614. https://doi.org/10.29106/fesa.422113
Karasu, S., Altan, A., Saraç, Z., Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data. 2018 26th Signal Processing and Communications Applications Conference (SIU), 1–4. https://doi.org/10.1109/SIU.2018.8404760
Kartal, C. (2020). Bitcoin Fiyatlarinin K-Star Algoritmasi İle Modellenmesi. BMIJ, 8(1), 213–231. https://doi.org/10.15295/bmij.v8i1.1380
Karthika, S., Margaret, V., Balaraman, K. (2017, 21-22 April 2017). Hybrid short term load forecasting using ARIMA-SVM. Paper presented at the 2017 Innovations in Power and Advanced Computing Technologies (i-PACT).
Kaytez, F. (2012). En Küçük Kareler Destek Vektör Makineleri ile Türkiye’nin Uzun Dönem Elektrik Tüketim Tahmini ve Modellemesi. Doktora Tezi, Gazi Üniversitesi.
Kaytez, F., Taplamacioglu, M. C., Cam, E., Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438. https://doi.org/10.1016/j.ijepes.2014.12.036
Kristjanpoller, W., Minutolo, M. C. (2018). A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Systems with Applications, 109, 1–11. https://doi.org/10.1016/J.ESWA.2018.05.011
Lahmiri, S., Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35–40. https://doi.org/10.1016/J.CHAOS.2018.11.014
Li, X., Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49–60. https://doi.org/10.1016/J.DSS.2016.12.001
Mallqui, D. C. A., Fernandes, R. A. S. (2019). Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing Journal, 75, 596–606. https://doi.org/10.1016/j.asoc.2018.11.038
Munim, Z. H., Shakil, M. H., Alon, I. (2019). Next-Day Bitcoin Price Forecast. Journal of Risk and Financial Management, 12(2), 1 – 15. https://doi.org/10.3390/jrfm12020103
Nakamoto, S. (2008). Bitcoin: a peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf
Pele, D. T., Mazurencu-Marinescu-Pele, M. (2019). Using High-Frequency Entropy to Forecast Bitcoin’s Daily Value at Risk. Entropy, 21(2). https://doi.org/10.3390/e21020102
Rane, P. V., Dhage, S. N. (2019). Systematic Erudition of Bitcoin Price Prediction using Machine Learning Techniques. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) Systematic.
Şahin, E. E. (2018). Crypto Money Bitcoin: Price Estimation With ARIMA and Artificial Neural Networks. Fiscaoeconomia, 2(2), 74–92. https://doi.org/10.25295/fsecon.2018.02.005
Şahin, E. E., Özkan, O. (2018). Asimetrik Volatilitenin Tahmini : Kripto Para Bitcoin Estimation Of Asymmetric Volatility : Crypto Money Application. Bilecik Şeyh Edebali Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(2), 240–247.
Selgin, G. (2015). Synthetic commodity money. Journal of Financial Stability, 17, 92–99. https://doi.org/10.1016/J.JFS.2014.07.002
Sun, X., Liu, M., Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 1 – 6. https://doi.org/10.1016/J.FRL.2018.12.032
Suykens, J. A. K., Vandewalle, J. (1999). Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9(3), 293-300. https://doi.org/10.1023/a:1018628609742
Troster, V., Tiwari, A. K., Shahbaz, M., Macedo, D. N. (2019). Bitcoin returns and risk: A general GARCH and GAS analysis. Finance Research Letters, 30, 187–193. https://doi.org/10.1016/J.FRL.2018.09.014
Walther, T., Klein, T., Bouri, E. (2019). Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money, 63, 101 – 133. https://doi.org/10.1016/j.intfin.2019.101133
Wołk, K. (2019). Advanced social media sentiment analysis for short-term cryptocurrency price prediction. Expert Systems, e12493. https://doi.org/10.1111/exsy.12493
Xuemei, L., Jin-hu, L., Lixing, D., Gang, X., Jibin, L. (2009). Building Cooling Load Forecasting Model Based on LS-SVM. 55-58. Paper presented at the 2009 Asia-Pacific Conference on Information Processing, https://doi.org/10.1109/apcip.2009.22

Turkish Studies - Economics, Finance, Politics-Cover

ISSN: 2667-5625
Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2006
Yayıncı: ASOS Eğitim Bilişim Danışmanlık Otomasyon Yayıncılık Reklam Sanayi ve Ticaret LTD ŞTİ