Abstract
Blockchain is distributed database provides encrypted transaction tracking. Similarly, Crypto money or cryptocurrency can be defined as digital currency or asset designed as alternative exchange tool, which uses cryptography to secure transactions based on blockchain database systematic is traded like cash. Moreover, without having central control system and authority, Bitcoin attracts investors more and more with this feature. In this context, the importance of this study may well explain factors affecting cryptocurrency prices in Bitcoin specific. It is used while choosing variables that determine Bitcoin prices, not only financial and economic factors, but also Bitcoin mining and Bitcoin Google Trend Index. While electricity unit costs from variables are included in model design; It is thought that electrical energy costs consumed by CPUs and GPUs of computers used by Bitcoin miners may have impact on Bitcoin prices In this study, bitcoin prices were modeled primarily with Box-Jenkins method with using between 2015-2020 daily data. In this study, it has been conducted with chosen control variables which are Gold unit prices, oil prices, Euro / Dollar parity, S&P 500 Index, LIBOR, Bitcoin Google Trend Index and electricity unit costs were determined as control variables and their effect on bitcoin prices was analyzed based on the Box-Jenkins model. Box-Jenkins modelling is chosen because this model has more power on forecasting future values with using AR and MA process together.
Keywords
References
- Bouri, Molnár, Azzi, Roubaud, & Hagfors, 2017. “On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?”. Finance Research Letters, 20, 192-198.
- Brandvold, Molnár, Vagstad, & Valstad, 2015. “Price discovery on Bitcoin exchanges”. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
- Chan, 1993. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The annals of statistics, 21(1), 520-533.
- Chan, Le, & Wu, 2019. “Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin. The Quarterly Review of Economics and Finance”, 71, 107-113.
- Ciaian, Rajcaniova, & Kancs, 2016. “The economics of Bitcoin price formation”. Applied Economics, 48(19), 1799-1815. Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018. “Exploring the dynamic relationships between cryptocurrencies and other financial assets”. Economics Letters, 165, 28-34.
- De Vries, 2018. “Bitcoin's growing energy problem”. Joule, 2(5), 801-805.
- Dyhrberg, 2016. “Hedging capabilities of bitcoin. Is it the virtual gold?”. Finance Research Letters, 16, 139-144.
- Enders, & Siklos, 2001. “Cointegration and threshold adjustment”. Journal of Business & Economic Statistics, 19 (2), 166-176.
- Hayes, 2015. “A cost of production model for bitcoin”. Available at SSRN 2580904. ristoufek, 2013. “Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era”. Scientific reports, 3, 3415.
- Parino, Beiró, & Gauvin, 2018. “Analysis of the Bitcoin blockchain: socio-economic factors behind the adoption”. EPJ Data Science, 7 (1), 38.
- Sert, T., 2019. Sorularla Blockchain. Türkiye Bilişim Vakfı, İstanbul.
Abstract
Blockchain dağıtılmış veritabanı şifreli işlem takibi sağlayan bir sistemdir. Benzer şekilde, Kripto para nakit gibi işlem gören blockchain veritabanı sistematiğine dayalı, işlemleri güvence altına almak için kriptografi kullanan ve alternatif takas aracı olarak tasarlanmış dijital para birimi olarak tanımlanabilir. Bitcoin’in merkezi kontrol sistemi olmayan bu özelliği yatırımcıları giderek daha çok cezbetmektedir. Bu bağlamda bu çalışma, Bitcoin özelinde kripto para fiyatlarını etkileyen faktörleri açıklayabilir. Bitcoin fiyatlarını belirleyen değişkenlerin seçiminde, finansal ve ekonomik faktörlerin yanı sıra, Bitcoin madenciliği ve Bitcoin Google Trend Endeksi gibi değişkenlerde kullanılmıştır. Model tasarımında değişkenlerden elektrik birim maliyetleri yer alırken; Bitcoin madencileri tarafından kullanılan bilgisayarların CPU'ları tarafından tüketilen elektrik enerjisi maliyetlerinin Bitcoin fiyatları üzerinde etkili olabileceği düşünülmektedir. Bu çalışmada 2015-2020 arası günlük veriler kullanılarak Bitcoin fiyatları öncelikle Box-Jenkins yöntemi ile modellenmiştir. Çalışmada, kontrol değişkenler olarak Altın fiyatları, petrol fiyatları, Euro / Dolar paritesi, S&P 500 Endeksi, LIBOR, Bitcoin Google Trend Endeksi ve elektrik birim maliyetleri kullanılmıştır. Bu modelin seçilmesinin nedeni AR ve MA sürecini birlikte kullanarak gelecekteki değerleri tahmin etmede daha fazla güce sahip olmasıdır.
Keywords
References
- Bouri, Molnár, Azzi, Roubaud, & Hagfors, 2017. “On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?”. Finance Research Letters, 20, 192-198.
- Brandvold, Molnár, Vagstad, & Valstad, 2015. “Price discovery on Bitcoin exchanges”. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
- Chan, 1993. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The annals of statistics, 21(1), 520-533.
- Chan, Le, & Wu, 2019. “Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin. The Quarterly Review of Economics and Finance”, 71, 107-113.
- Ciaian, Rajcaniova, & Kancs, 2016. “The economics of Bitcoin price formation”. Applied Economics, 48(19), 1799-1815. Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018. “Exploring the dynamic relationships between cryptocurrencies and other financial assets”. Economics Letters, 165, 28-34.
- De Vries, 2018. “Bitcoin's growing energy problem”. Joule, 2(5), 801-805.
- Dyhrberg, 2016. “Hedging capabilities of bitcoin. Is it the virtual gold?”. Finance Research Letters, 16, 139-144.
- Enders, & Siklos, 2001. “Cointegration and threshold adjustment”. Journal of Business & Economic Statistics, 19 (2), 166-176.
- Hayes, 2015. “A cost of production model for bitcoin”. Available at SSRN 2580904. ristoufek, 2013. “Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era”. Scientific reports, 3, 3415.
- Parino, Beiró, & Gauvin, 2018. “Analysis of the Bitcoin blockchain: socio-economic factors behind the adoption”. EPJ Data Science, 7 (1), 38.
- Sert, T., 2019. Sorularla Blockchain. Türkiye Bilişim Vakfı, İstanbul.
Details
| Primary Language | English |
|---|---|
| Journal Section | Makaleler |
| Authors | |
| Publication Date | March 27, 2021 |
| Published in Issue | Year 2021 Volume: 25 Issue: 1 |
