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Bitcoin Fiyat Hareketleri Üzerine: ARIMA ile Kısa Vadeli Bir Fiyat Tahmini

Year 2021, Volume: 8 Issue: 2, 293 - 309, 04.08.2021

Abstract

Kripto paraların günlük işlem hacmi Bitcoin öncülüğündeki artış eğilimini uzun zamandır sürdürürken 2012’den bugüne, günlük işlem miktarı 7 binden 1 milyona ulaşmıştır. Bu çalışma Bitcoin özelinde kripto paralardan beklenen faydanın ne olduğunu inceleyip, fiyat dalgalanmaları ve volatilitenin öngörülebilirlik derecesini tespit etmeyi amaçlamaktadır. Çünkü yüksek getiri vaadeden bu piyasaya dönük risk iştahı ve getiri maksimizasyonu ikilemi, kurumsal yatırımcılar için özellikle sıfır alt sınırı ve negatif getiri ortamında belirginleşmiştir. Dolayısıyla Bitcoin’in kısa vadeli fiyat hareketlerinin öngörülebilirliği, fiyat formasyonuna da ışık tutacaktır. Çalışmada ARIMA yaklaşımının tercih edilme nedeni kısa vadeli tahminlerdeki duyarlılığıdır. ARIMA (1,1,0) Bitcoin’in 2020Q3 ve 2020Q4 dönemlerindeki fiyat hareketlerini istatistiki olarak isabetli tahmin etmiş ve modelde görülen sapmalar, yatırımcıların Bitcoin için dijital bir varlık olması yönünde değişen, yatırım değeri algıları ile ilişkilendirilmiştir.

References

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On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA

Year 2021, Volume: 8 Issue: 2, 293 - 309, 04.08.2021

Abstract

Daily transactions in cryptocurrencies have long been following an ascending tendency, with Bitcoin leading the charge. Daily transactions recorded in the system increased from 7000 trade per day in 2012to more than 1 million nowadays. The study aims to examine the utility of cryptocurrencies specific to Bitcoin and diagnose how predictable its price fluctuations and the volatility of the crypto market. Because the dilemma between risk aversion and return maximization became evident for investors with high yielded digital assets in a zero-lower bound environment. Hence the predictability of its price movements in the short run may shed some light on the price formation of Bitcoin. Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19. 

References

  • Aalborg, H. A., Molnar, P., & Erik de Vries, J.. (2019). What can explain the price, volatility and trading volume of bitcoin?. FinanceResearchLetters, Vol. 29, 255-265. google scholar
  • Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting. 8(1), 69-80. google scholar
  • Androulaki E., Karame G.O., Roeschlin M., Scherer T., & Capkun S. (2013) Evaluating User Privacy in Bitcoin. In: Sadeghi AR. (ed) Financial cryptography and data security (pp 34-51. FC 2013. Lecture Notes in Computer Science, vol 7859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39884-1_4. google scholar
  • Azari, A. (2019). Bitcoin price prediction: an ARIMA approach. Social and Information Networks, arXiv:1904.05315. google scholar
  • Baig, A., Blau, B. M., & Sabah, N.. (2019). Price clustering and sentiment in bitcoin. Finance Research Letters, Vol. 29, 111-116. google scholar
  • Balcilar, M., Bourid, E., Guptac, R., & Roubaud, D. (2017). Can volume predict bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, No. 64, 74-81. google scholar
  • Bhutoria, R. (2021). 2020 Bitcoin Retrospective. Fidelity Digital Assets Researches. Retrieved from https://www. fidelitydigitalassets.com/articles/2020-bitcoin-retrospective. google scholar
  • Bhutoria, R. (2020). Bitcoin investment thesis: bitcoin’s role as an alternative investment. Fidelity Digital Assets Researches. Retrieved from https://www.fidelitydigitalassets.com/bin-public/060_www_fidelity_com/ documents/FDAS/bitcoin-alternative-investment.pdf. google scholar
  • Bhutoria, R., & McCurdy, T. (2020). Why corporate treasurers may consider bitcoin. Fidelity Digital Assets Researches. Retrieved from https://www.fidelitydigitalassets.com/articles/corporate-treasurer-bitcoin?ccmedia =owned&ccchannel=email&cccampaign=newsletter&cctactics=newsletter_dec. google scholar
  • Box, G. E. P., & Jenkins, M. G. (1976). Time series analysis forecasting and control, 2nd edition. San Francisco: Holden-Day. google scholar
  • Bouri, E., Azzi, Georges, & 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 (2017-2), 1-16. google scholar
  • Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238. google scholar
  • Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits and bets, information, price volatility, and demand for bitcoin. Economics 312. Retrieved from http://www.bitcointrading.com/pdf/bitsandbets.pdf. google scholar
  • Bukovina, J., & Marticek, M. (2016). Sentiment and bitcoin volatility. MENDELU Working Papers in Business and Economics, No: 58, Mendel University, Brno. google scholar
  • Blockchain.com. (2021). Explorer. Retrieved from https://www.blockchain.com/explorer. google scholar
  • CBN. (May 2021). Chinese central bank aids in development of two new blockchain standards. Retrieved from https://www.chinabankingnews.com/2021/05/12/chinese-central-bank-aids-in-development-of-two-new-blockchain-standards/. google scholar
  • CBN. (May 2021). China’s Top Financial Authority Calls for Crackdown on Bitcoin Mining and Trading. Retrieved from https://www.chinabankingnews.com/2021/05/22/chinas-top-financial-authority-calls-for-crackdown-on-bitcoin-mining-and-trading/. google scholar
  • Ciaian, P., Rajcaniova, M., & Kancs, d’Artis. (2016). The economics of bitcoin price formation. Applied Economics, 48 (19), 1799-1815. google scholar
  • Dickey, A. D., & Fuller, A. W. (1979). Distribution of the estimators for autoregressive time series models with a unit root. Journal of the American Statistical Association, 74(366), 427-431. google scholar
  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar -A GARCH volatility analysis. Finance Research Letters, Vol. 16, pp. 85-92. google scholar
  • Fang, L., Bourib, E., Guptac, R., & Roubaud, D. (2019). Does global economic uncertainty matter for the volatility and hedging effectiveness of bitcoin. International Review of Financial Analysis, Vol. 61, pp 29-36. google scholar
  • Hyndman, J. R., & Koehler, B. A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. google scholar
  • Hyndman, J. R., & Athanasopoulos, G. (2018). Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. Retrieved from OTexts.com/fpp2. google scholar
  • Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898. https://doi.org/10.3390/math7100898. google scholar
  • Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679. google scholar
  • Klein, T., Thu, H. P., & Walter, T. (2018). Bitcoin is not the new gold - a comparison of volatility, correlation, and portfolio performance. International Review ofFinancial Analysis, Vol. 59, 105-116. google scholar
  • 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://doi.org/10.1038/srep03415. google scholar
  • Lewis, C. D. (1982). International and business forecasting methods: a practical guide to exponential smoothing and curve fitting. London. Butterworth Scientific. google scholar
  • Makridakis, S., Hibon, M., & Moser, C. (1979). Accuracy of forecasting: an empirical investigation. Journal of the Royal Statistical Society, 142(2), 97-145. google scholar
  • Manoukian, J. (2021). It was a wild week for bitcoin. J.P.Morgan Insights. Retrieved from https://www.jpmorgan. com/wealth-management/wealth-partners/insights/it-was-a-wild-week-for-bitcoin. google scholar
  • McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of bitcoin using machine learning. 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 339343. https://doi.org/10.1109/PDP2018.2018.00060. google scholar
  • Moreno, J. J. M., Pol, Al. P., Abad, A. S., & Blasco, B. C.. (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), 500-506. google scholar
  • Munim, Z. H., Shakil, Mohammad H., & Alon, I. (2019). Next-day bitcoin price forecast. Journal of Risk and Financial Management, 12(2), 103. https://doi.org/10.3390/jrfm12020103. google scholar
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. White Paper. Retrieved from https://www. ussc.gov/sites/default/files/pdf/training/annual-national-training-seminar/2018/Emerging_Tech_Bitcoin_ Crypto.pdf. google scholar
  • Nakamoto, S. (2009). Bitcoin open source implementation of p2p currency. Retrieved from https://satoshi. nakamotoinstitute.org/posts/p2pfoundation/1/. google scholar
  • Pichl, L., & Kaizoji, T. (2017). Volatility analysis of bitcoin price time series. Quantitative Finance and Economics, 1(4), 474-485. google scholar
  • Senner, R. & Sornette, D. (2019). “The Holy Grail of Crypto Currencies: Ready to Replace Fiat Money?”, Journal of Economic Issues, Vol. 53(4), 966-1000. google scholar
  • Shen, D., Urquhart, A., & Wang, P. (2019). Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks. European Financial Management, 26(5), 1294-1323. google scholar
  • Statistica. (2021). Market capitalization of bitcoin. Retrieved from https://www.statista.com/statistics/377382/ bitcoin-market-capitalization/. google scholar
  • Best, R. (2021). Bitcoin (BTC) market capitalization as ofMay 17, 2021. Retrieved from https://www.statista.com/ statistics/377382/bitcoin-market-capitalization/. google scholar
  • Twarakavi, M., & Bansal, Y. (2020). Bitcoin price prediction: a comparative study. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 9(5), 147-150. google scholar
  • Yen, K., & Cheng, H. (2021). Economic policy uncertainty and cryptocurrency volatility. Finance Research Letters, Vol. 38, 101428. https://doi.org/10.1016/j.frl.2020.101428. google scholar
  • Variankaval, R., Junek, E., Saperia, A., Richards, H., & Moy, C. (2018). Blockchain and the decentralization revolution: A CFO’s guide to the potential ımplications of distributed ledger technology, J.P. Morgan. Retrieved from https://www.jpmorgan.com/solutions/cib/investment-banking/corporate-finance-advisory/blockchain. google scholar
There are 43 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Makaleler
Authors

Mohamed Khalil Benzekri 0000-0002-0898-250X

Hatice Şehime Özütler 0000-0002-2213-3483

Publication Date August 4, 2021
Submission Date May 31, 2021
Published in Issue Year 2021 Volume: 8 Issue: 2

Cite

APA Benzekri, M. K., & Özütler, H. Ş. (2021). On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. İktisat Politikası Araştırmaları Dergisi, 8(2), 293-309.
AMA Benzekri MK, Özütler HŞ. On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. JEPR. August 2021;8(2):293-309.
Chicago Benzekri, Mohamed Khalil, and Hatice Şehime Özütler. “On the Predictability of Bitcoin Price Movements: A Short-Term Price Prediction With ARIMA”. İktisat Politikası Araştırmaları Dergisi 8, no. 2 (August 2021): 293-309.
EndNote Benzekri MK, Özütler HŞ (August 1, 2021) On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. İktisat Politikası Araştırmaları Dergisi 8 2 293–309.
IEEE M. K. Benzekri and H. Ş. Özütler, “On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA”, JEPR, vol. 8, no. 2, pp. 293–309, 2021.
ISNAD Benzekri, Mohamed Khalil - Özütler, Hatice Şehime. “On the Predictability of Bitcoin Price Movements: A Short-Term Price Prediction With ARIMA”. İktisat Politikası Araştırmaları Dergisi 8/2 (August 2021), 293-309.
JAMA Benzekri MK, Özütler HŞ. On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. JEPR. 2021;8:293–309.
MLA Benzekri, Mohamed Khalil and Hatice Şehime Özütler. “On the Predictability of Bitcoin Price Movements: A Short-Term Price Prediction With ARIMA”. İktisat Politikası Araştırmaları Dergisi, vol. 8, no. 2, 2021, pp. 293-09.
Vancouver Benzekri MK, Özütler HŞ. On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. JEPR. 2021;8(2):293-309.