Araştırma Makalesi
BibTex RIS Kaynak Göster

BAŞLICA ETKİN KRİPTO PARALARDA OYNAKLIK ANALİZİ

Yıl 2024, Cilt: 38 Sayı: 1, 161 - 183, 29.03.2024
https://doi.org/10.48070/erciyesakademi.1401745

Öz

Bitcoin'in 2009 yılında ortaya çıkmasıyla birlikte, birçok sektör üzerindeki etkileri gözlemlenmiştir. Ancak, kripto para piyasalarındaki yüksek volatilite ve merkezi bir kontrol olmaması, kripto paraların geleceği konusunda belirsizlik yaratmaktadır. Bu anlamda, finansal sektörlerin dinamik yapısı gereği diğer sektörlerden daha hızlı etkilendikleri doğal olarak kabul edilmektedir. Bu araştırmanın temel amacı, Bitcoin, Ethereum, Litecoin ve Ripple gibi dört kripto para biriminin yatırım aracı olarak potansiyelini değerlendirmektir. Bu amaç doğrultusunda, 1 Ocak 2018 - 1 Ocak 2023 tarihleri arasında, seçili kripto para birimlerinin getiri oranlarının volatilite özellikleri modellenmeye çalışılmıştır. Otoregresif koşullu değişen varyans modelleri (Autoregressive conditional heteroskedasticity - ARCH) analizi kullanılarak yapılan çalışmada, modelin volatilite tahmininin anlamlı sonuçlar vermesi üzerine VAR analizi ve Granger nedensellik ilişkileri eklenerek desteklenmiştir. Bu testlerin sonucunda kripto para birimlerinin risk profili incelenmiş ve gelecekteki fiyat hareketlerine ilişkin bir tahmin sağlanması amaçlanmıştır. Bu şekilde, kripto para birimlerinin potansiyel bir yatırım aracı olarak değerlendirilmesi konusunda tespitler yapılarak literatüre katkıda bulunulmuştur. Bu bağlamda, serilerde ARCH etkisi gözlemlenmiştir. Yapılan VAR ve Granger Nedensellik testleri sonucunda, Bitcoin'deki bir değişikliğin diğer altcoin'leri önemli ölçüde etkilediği ancak Ripple'da anlamlı bir etkinin olmadığı sonucuna varılmıştır.

Kaynakça

  • Akyüz, H. E. (2018). Vektör otoregresyon (VAR) modeli ile iklimsel değişkenlerin istatistiksel analizi, Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 10(2), 184-192. https://doi.org/10.29137/umagd.402272
  • Almansour, B. Y., Almansour, A., & Alshater, M. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139. https://doi.org/10.7232/iems.2021.20.2.130
  • Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Economics Letters, 22(1) 30-34. https://doi.org/10.1080/13504851.2014.916379
  • Belinky, M., Veitch, A., & Rennick, E. (2015). The Fintech 2.0 paper: Rebooting financial services. 1-20.
  • Cameron Dark, Emery, D., Ma, J., & Noone, C. (2019). Cryptocurrency: Ten years on. Reserve Bank of Australia, Bulletin, 195-214.
  • Ceyhan, V., & Gündüz, O. VEKTÖR OTOREGRESYON MODELLERİ. https://avys.omu.edu.tr/storage/app/public/vceyhan/109840/VAR.pdf adresinden alındı
  • Chakravorty, C., & Gowda, N. (2021). Comparative study on cryptocurrency transaction and banking transaction. Global Transitions Proceedings 2, 530–534 . https://doi.org/10.1016/j.gltp.2021.08.064
  • Davoodalhosseini, M., Chiu, J., Jiang, J., & Zhu, Y. (2022). Bank market power and central bank digital currency: Theory and quantitative assessment. Bank of Canada Staff Working Paper 2019-20. Funds Management and Banking Department Ottawa, Ontario, Canada K1A 0G9, 1-27.
  • Erdem, E. (2021). Para banka ve finansal sistem (11. Baskı). Detay Yayınevi.
  • Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom. Econometrica, 50(4), 987-1007.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., N., D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices. 9th Mediterranean Conference on Information Systems At: Samos, Greece, Department of Management Science, 1-14. https://dx.doi.org/10.2139/ssrn.2607167
  • Gvozdenović, N., Marcikić, A., & Radovanov, B. (2016). A time series analysis of four major cryptocurrencies. Facta Universitatis, Series: Economics and Organization, 15(3), 271-278. https://doi.org/10.22190/FUEO1803271R
  • Hanisoglu, G. S., Kizil, C., & Aslan, T. (2019). Kripto paraların finansal piyasalara etkileri ve muhasebeleştirilmesi. Bursa: Ekin Basım Yayın Dağıtım.
  • James, H., & Brunnermeier, M. K. (2019). The digitalization of money. Princeton University, 1-32. http://www.nber.org/papers/w26300
  • Karadeniz, Ö. (2021). Analysis of the relationship between cryptocurrencies and Borsa Istanbul: Before and After Covid-19. Istanbul Bilgi University Institute of Social Science Financial Economics Master's Degree Program.
  • Kim, O., Ariane, S., & Marie, B. (2015). Virtual currency, tangible return: portfolio diversification with bitcoin. Journal of Asset Management,16(6), 365-373. https://doi.org/ 10.1057/jam.2015.5
  • Meegan, A., McHugh, G., & Corbet, S. (2017). The influence of Central Bank monetary policy announcements on cryptocurrency return volatility. Investment Management and Financial Innovations, 14(4), 60-72. https://doi.org/10.21511/imfi.14(4).2017.07
  • Mullan, P. C. (2016). A history of digital currency in United States. Palgrave Advances in the Economics of Innovation and Technology, Virginia , USA. 1-278.
  • Nadarajah, S., Chan, S., & Chu, J. (2015). Statistical analysis of the exchange rate of bitcoin. Plos One, School of Mathematics, University of Manchester, Manchester M13 9PL, United Kingdom,10(7), 1-27. https://doi.org/10.1371/journal.pone.0133678
  • Pickford, S. (2021, ocak 29). Digital currencies: Economic and geopolitical challenges. https://www.chathamhouse.org/2021/01/digital-currencies-economic-and-geopolitical challenges? gclid=CjwKCAjwnZaVBhA6EiwAVVyv9DWcew2hhfJ2-vUgYOadSK JRCsA1hFDqwXD3tm5CLFtYHRArGpUBxoCFNQQAvD_BwE
  • Takım, A. (2010). Türkiye’de GSYİH ile ihracat arasındaki ilişki: Granger nedensellik testi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 14(2), 315-330.
  • Times, T. E. (2021). Cryptocurrency is gaining worldwide acceptance, Here are 5 reasons why. https://economictimes.indiatimes.com/markets/cryptocurrency/cryptocurrency-is-gaining-worldwide- acceptance-here-are-5-reasons-why/articleshow/87209465.cms
  • Underwood, S. (2016). Blockchain beyond bitcoin. Communications of the ACM, 59(11), 15-17. https://doi.org/10.1145/2994581
  • Wang, C. (2021). Different GARCH model analysis on returns and volatility in bitcoin . Management School, Liverpool University, London City, United Kingdom,1(1), 37-59. https://doi.org/10.3934/DSFE.2021003
  • Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 21-24. https://doi.org/10.1016/j.econlet.2018.04.003
  • Yermack, D. (2013). Is Bitcoin a real currency? Department of Finance, New York University Stern School of Business, 1-14. http://dx.doi.org/10.2139/ssrn.2361599.

VOLATILITY ANALYSIS IN THE MAIN ACTIVE CRYPTOCURRENCIES

Yıl 2024, Cilt: 38 Sayı: 1, 161 - 183, 29.03.2024
https://doi.org/10.48070/erciyesakademi.1401745

Öz

With the emergence of Bitcoin in 2009, its effects on various sectors have been observed. However, the high volatility and the absence of centralized control in cryptocurrency markets create uncertainty about the future of cryptocurrencies. In this regard, due to the dynamic nature of financial sectors, they are naturally presumed to be more rapidly affected than other sectors. The primary aim of this research is to evaluate the investment potential of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. Towards this aim, between January 1, 2018, and January 1, 2023, the volatility characteristics of selected cryptocurrencies' return rates have been attempted to be modeled. Using Autoregressive Conditional Heteroskedasticity (ARCH) analysis, the volatility prediction of the model yielded significant results, further supported by VAR analysis and Granger causality relationships. As a result of these tests, the risk profile of cryptocurrencies has been examined, aiming to provide a forecast for future price movements. In this way, findings have been made regarding the evaluation of cryptocurrencies as a potential investment vehicle, contributing to the literature. In this context, ARCH effects have been observed in the series. Based on the VAR and Granger Causality tests, it has been concluded that while changes in Bitcoin significantly influence other altcoins, Ripple does not exhibit a significant effect.

Kaynakça

  • Akyüz, H. E. (2018). Vektör otoregresyon (VAR) modeli ile iklimsel değişkenlerin istatistiksel analizi, Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 10(2), 184-192. https://doi.org/10.29137/umagd.402272
  • Almansour, B. Y., Almansour, A., & Alshater, M. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139. https://doi.org/10.7232/iems.2021.20.2.130
  • Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Economics Letters, 22(1) 30-34. https://doi.org/10.1080/13504851.2014.916379
  • Belinky, M., Veitch, A., & Rennick, E. (2015). The Fintech 2.0 paper: Rebooting financial services. 1-20.
  • Cameron Dark, Emery, D., Ma, J., & Noone, C. (2019). Cryptocurrency: Ten years on. Reserve Bank of Australia, Bulletin, 195-214.
  • Ceyhan, V., & Gündüz, O. VEKTÖR OTOREGRESYON MODELLERİ. https://avys.omu.edu.tr/storage/app/public/vceyhan/109840/VAR.pdf adresinden alındı
  • Chakravorty, C., & Gowda, N. (2021). Comparative study on cryptocurrency transaction and banking transaction. Global Transitions Proceedings 2, 530–534 . https://doi.org/10.1016/j.gltp.2021.08.064
  • Davoodalhosseini, M., Chiu, J., Jiang, J., & Zhu, Y. (2022). Bank market power and central bank digital currency: Theory and quantitative assessment. Bank of Canada Staff Working Paper 2019-20. Funds Management and Banking Department Ottawa, Ontario, Canada K1A 0G9, 1-27.
  • Erdem, E. (2021). Para banka ve finansal sistem (11. Baskı). Detay Yayınevi.
  • Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom. Econometrica, 50(4), 987-1007.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., N., D., & Giaglis, G. M. (2015). Using time-series and sentiment analysis to detect the determinants of bitcoin prices. 9th Mediterranean Conference on Information Systems At: Samos, Greece, Department of Management Science, 1-14. https://dx.doi.org/10.2139/ssrn.2607167
  • Gvozdenović, N., Marcikić, A., & Radovanov, B. (2016). A time series analysis of four major cryptocurrencies. Facta Universitatis, Series: Economics and Organization, 15(3), 271-278. https://doi.org/10.22190/FUEO1803271R
  • Hanisoglu, G. S., Kizil, C., & Aslan, T. (2019). Kripto paraların finansal piyasalara etkileri ve muhasebeleştirilmesi. Bursa: Ekin Basım Yayın Dağıtım.
  • James, H., & Brunnermeier, M. K. (2019). The digitalization of money. Princeton University, 1-32. http://www.nber.org/papers/w26300
  • Karadeniz, Ö. (2021). Analysis of the relationship between cryptocurrencies and Borsa Istanbul: Before and After Covid-19. Istanbul Bilgi University Institute of Social Science Financial Economics Master's Degree Program.
  • Kim, O., Ariane, S., & Marie, B. (2015). Virtual currency, tangible return: portfolio diversification with bitcoin. Journal of Asset Management,16(6), 365-373. https://doi.org/ 10.1057/jam.2015.5
  • Meegan, A., McHugh, G., & Corbet, S. (2017). The influence of Central Bank monetary policy announcements on cryptocurrency return volatility. Investment Management and Financial Innovations, 14(4), 60-72. https://doi.org/10.21511/imfi.14(4).2017.07
  • Mullan, P. C. (2016). A history of digital currency in United States. Palgrave Advances in the Economics of Innovation and Technology, Virginia , USA. 1-278.
  • Nadarajah, S., Chan, S., & Chu, J. (2015). Statistical analysis of the exchange rate of bitcoin. Plos One, School of Mathematics, University of Manchester, Manchester M13 9PL, United Kingdom,10(7), 1-27. https://doi.org/10.1371/journal.pone.0133678
  • Pickford, S. (2021, ocak 29). Digital currencies: Economic and geopolitical challenges. https://www.chathamhouse.org/2021/01/digital-currencies-economic-and-geopolitical challenges? gclid=CjwKCAjwnZaVBhA6EiwAVVyv9DWcew2hhfJ2-vUgYOadSK JRCsA1hFDqwXD3tm5CLFtYHRArGpUBxoCFNQQAvD_BwE
  • Takım, A. (2010). Türkiye’de GSYİH ile ihracat arasındaki ilişki: Granger nedensellik testi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 14(2), 315-330.
  • Times, T. E. (2021). Cryptocurrency is gaining worldwide acceptance, Here are 5 reasons why. https://economictimes.indiatimes.com/markets/cryptocurrency/cryptocurrency-is-gaining-worldwide- acceptance-here-are-5-reasons-why/articleshow/87209465.cms
  • Underwood, S. (2016). Blockchain beyond bitcoin. Communications of the ACM, 59(11), 15-17. https://doi.org/10.1145/2994581
  • Wang, C. (2021). Different GARCH model analysis on returns and volatility in bitcoin . Management School, Liverpool University, London City, United Kingdom,1(1), 37-59. https://doi.org/10.3934/DSFE.2021003
  • Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 21-24. https://doi.org/10.1016/j.econlet.2018.04.003
  • Yermack, D. (2013). Is Bitcoin a real currency? Department of Finance, New York University Stern School of Business, 1-14. http://dx.doi.org/10.2139/ssrn.2361599.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Para Politikası
Bölüm Makaleler
Yazarlar

Lokman Salih Erdem 0000-0001-6423-0874

Hayriye Atik

Erken Görünüm Tarihi 26 Mart 2024
Yayımlanma Tarihi 29 Mart 2024
Gönderilme Tarihi 7 Aralık 2023
Kabul Tarihi 13 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 38 Sayı: 1

Kaynak Göster

APA Erdem, L. S., & Atik, H. (2024). BAŞLICA ETKİN KRİPTO PARALARDA OYNAKLIK ANALİZİ. Erciyes Akademi, 38(1), 161-183. https://doi.org/10.48070/erciyesakademi.1401745

ERCİYES AKADEMİ | 2021 | erciyesakademi@erciyes.edu.tr Bu eser Creative Commons Atıf-Gayri Ticari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.