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Rynek kryptowalut – sytuacja bieżąca i kierunki rozwoju

100%
Wikarczyk A.
Studia BAS
|
2019
|
issue 1(57)
143-160
EN
The article provides a general overview of blockchain technology and its influence on cryptocurrency development. It is based on asymmetric cryptology, peer-to-peer technology, hash functions, consensus mechanism, and smart contracts. Since 2008, when Satoshi Nakamoto published online the Bitcoin blueprint, cryptocurrency market has grown. There are more than 1000 cryptocurrencies now (e.g. Ethereum, Dogecoin, Monero), as well as many stock exchange markets, exchange offices and bitcoin cash machines. It is also possible to “mine” cryptocurrencies by yourself, however special hardware solutions are required. The author claims that cryptocurrency has redefined the way of thinking and viewing the concept of money, and it will expand even further.
2
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EFEKT ZARAŻANIA NA RYNKU KRYPTOWALUT

72%
Siwek-Skrzypek A.
Metody Ilościowe w Badaniach Ekonomicznych
|
2018
|
vol. 19
|
issue 2
162-170
PL
W artykule podjęto próbę odpowiedzenia na pytania: czy po wstrząsie, 5-go stycznia 2018 roku, na rynku kryptowalut wystąpił efekt zarażania oraz czy wahania kursu Bitcoina mają wpływ na kształtowanie się cen innych kryptowalut. Dodatkowo sprawdzono czy czynnik zewnętrzny ma wpływ na notowania kryptowalut. Badania przeprowadzono na danych z przełomu 2017 i 2018 roku. Do celów badawczych użyto modelu VAR-DCC-GARCH. Przeprowadzone analizy wykazały, że wystąpił efekt zarażania na badanym rynku oraz spadki zanotowane dla kryptowaluty Bitcoin, spowodowały spadki cen innych badanych kryptowalut.
EN
The aim of this paper was to answer two questions: whether the contagion effect occured in the cryptocurrency market after the shock of the 5th of January 2018 and whether the price changes observed for Bitcoin had an impact on other examined cryptocurrencies. This paper examined whether adding external factors affected the cryptocurrency market. Data used in this article were from the turn of 2017 and 2018. In addition, the VAR-DCC-GARCH model was employed for research purposes. Above all, this paper argued that contagion effect did occur in the market analysed. Secondly, the decreases recorded for Bitcoin cryptocurrency indeed caused price drops for other cryptocurrencies examined.
3
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Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

58%
Ślepaczuk R., Zenkova M.
Central European Economic Journal
|
2018
|
vol. 5
|
issue 52
EN
This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
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