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Journals / Süleyman Demirel Üniversitesi Vizyoner Dergisi / 2021 / Cilt: 12 - Sayı: 29

Investigation of the Relationship between Cryptocurrencies: Hatemi-J Asymmetric Causality Analysis

Kripto Paralar Arasındaki İlişkinin İncelenmesi: Hatemi-J Asimetrik Nedensellik Analizi

Nazan ŞAK
Pages
149–175
Publication Date
2021-02-20
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

While the most preferred crypto money in negative shock periods are Ripple, Binance coin, Bitcoin cash and Monero, in positive shock periods, they are Bitcoin, Ripple, Binance coin, Dash and Bitcoin cash.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:with the highest market value and volatility among crypto currencies produced by systems called block chains with special encryption mechanisms using mathematical algorithms.
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Key Findings & Data:In the study, 13 top-crypto currencies which are seen as an alternative to other investment instruments lately and whose data can be accessed in the relevant period, are used.
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Conclusion & Impact:While the most preferred crypto money in negative shock periods are Ripple, Binance coin, Bitcoin cash and Monero, in positive shock periods, they are Bitcoin, Ripple, Binance coin, Dash and Bitcoin cash.
🔬Design:Research Article
📍Scope:2017 Verileri
⚙️Variables:Kripto Para · Hatemi-J Asimetrik Nedensellik Testi · Blok Zincir

Abstract

with the highest market value and volatility among crypto currencies produced by systems called block chains with special encryption mechanisms using mathematical algorithms. Over time, many virtual currencies alternative to Bitcoin started to take place also in this system. In the study, 13 top-crypto currencies which are seen as an alternative to other investment instruments lately and whose data can be accessed in the relevant period, are used. The relationship between these coins during the winning and losing periods when positive and negative shocks are experienced is examined with Hatemi-J asymmetric causality test. For this purpose, the daily closing price data of Bitcoin, Ethereum, Ripple, Bitcoin cash, Litecoin, Eos, Binance coin, Stellar, Monero, Dash, Ethereum classic, Neo and Zcash crypto currencies between 26.7.2017-27.2.2020 are used. As a result of the analysis, it is stated that people diversify their investment tools especially in the periods that earned them; it is determined that they invest in crypto currencies, which are seen as less risky in the losing periods. While the most preferred crypto money in negative shock periods are Ripple, Binance coin, Bitcoin cash and Monero, in positive shock periods, they are Bitcoin, Ripple, Binance coin, Dash and Bitcoin cash.

Özet

Kripto paralar, teknolojideki ilerlemeler ile birlikte ilk ortaya çıktığı günden itibaren hızlı bir şekilde gelişme göstererek işlem görmeye başlamıştır. Matematiksel algoritmalar kullanılarak özel şifreleme mekanizmalarıyla blok zincir (blockchain) olarak adlandırılan sistemler ile üretilen kripto paralar içinde Bitcoin, en yüksek piyasa değerine ve işlem hacmine sahip sanal paradır. Zamanla Bitcoin’e alternatif birçok sanal para da bu sistem içinde yer almaya başlamıştır. Bu çalışmada, son dönemde diğer yatırım araçlarına alternatif olarak görülen kripto paralardan piyasa değeri olarak ilk 30 içinde yer alan ve ilgili dönemde verisine ulaşılabilen 13 kripto para kullanılmıştır. Pozitif ve negatif şokların yaşandığı kazandıran ve kaybettiren dönemlerde bu paralar arasındaki ilişki, Hatemi-J asimetrik nedensellik testiyle incelenmiştir. Bu amaçla, Bitcoin, Ethereum, Ripple, Bitcoin cash, Litecoin, Eos, Binance coin, Stellar, Monero, Dash, Ethereum classic, Neo ve Zcash kripto paralarının 26.7.2017-27.2.2020 tarihleri arasındaki günlük kapanış fiyatları verileri kullanılmıştır. Yapılan analiz sonucunda özellikle kazandıran dönemlerde kişilerin yatırım araçlarını çeşitlendirebildiği; kaybettiren dönemlerde ise daha az riskli olarak görülen kripto paralara yatırım yaptığı gözlenmiştir. Negatif şok dönemlerinde en çok tercih edilen kripto para Ripple, Binance coin, Bitcoin cash ve Monero iken; pozitif şok dönemlerinde Bitcoin, Ripple, Binance coin, Dash ve Bitcoin cash’dir.

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Cite This Article

ŞAK, N. (2021). Investigation of the Relationship between Cryptocurrencies: Hatemi-J Asymmetric Causality Analysis. Süleyman Demirel Üniversitesi Vizyoner Dergisi, Cilt: 12 - Sayı: 29, 149–175. https://ajindex.com/articles/163855

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References (13)

  1. Adana Karaağaç, G. ve Altınırmak, S. (2018). En yüksek piyasa değerine sahip on kripto paranın birbirleriyle etkileşimi. Muhasebe ve Finansman Dergisi, 79, 123-138.
  2. Algan, N., İşcan, E. ve Oktay, D. S. (2020). Economics of blockchain and impacts on economy: Rise of the crypto economy. S. Evci ve A. Sharma (Ed.). Studies at the Crossroads of Management & Economics içinde (177-186), London: IJOPEC Publication Limited. Anavatan, A. ve Yalçın Kayacan, E. (2019). Are bitcoin returns predictable?. Journal of Current Researches on Business and Economics, 9(1), 13-22.
  3. Attaran, M. ve Gunasekaran, A. (2019). Blockchain-enabled technology: the emerging technology set to reshape and decentralise many Industries. Int. J. Applied Decision Sciences, 12(4), 424-444.
  4. Balcılar, M, Bouri, E., Gupta, R. ve Roubaud, D. (2017). Can volume predict bitcoin returns and volatility? A Quantiles-Based Approach, Economic Modelling, 64, 74-81.
  5. Baur, D. G. ve Dimpfl, T. (2017). Realized bitcoin volatility. SSRN, 2949754,1-26.
  6. Böhme, R., Christin, N., Edelman, B. G. ve Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238.
  7. Ciaian, P., Rajcaniova, M. ve Kancs, d’A. (2018). Virtual relationships: Short and long run evidence from bitcoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173-195.
  8. Charles, A. ve Darne, O. (2018). Volatility estimation for bitcoin: Replication and robustness. International Economics, 157, 23-32.
  9. Chu, J., Chan, S., Nadarajah, S. ve Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(17), 1-15. doi:10.3390/jrfm10040017 Çetiner, M. (2018). Bitcoin (Kripto para) ve blok zincirin yeni dünyaya getirdikleri. İstanbul Journal of Social Sciences, 20, 1-16.
  10. CoinMarket cap. (2020). Top 100 Cryptocurrencies by Market Capitalization, Erişim adresi: https://coinmarketcap.com, (21.04.2020).
  11. Çütçü, İ. ve Kılıç, Y. (2018). Bitcoin fiyatları ile dolar kuru arasındaki ilişki: Yapısal kırılmalı zaman serisi analizi. Yönetim ve Ekonomi Araştırmaları Dergisi, 16(4), 349-366.
  12. Dilek, Ş. (2018). Blockchain Teknolojisi ve Bitcoin, 231, 1-32. Erişim adresi: https://setav.org/assets/uploads/2018/02/231.-Bitcoin.pdf, (21.04.2020).
  13. Dirican, C. ve Canoz, İ. (2017). The Cointegration relationship between bitcoin prices and major world stock indices: An analysis with ARDL model approach. Journal of Economics, Finance and Accounting, 4(4)