Aurelio F. Bariviera, Luciano Zunino, Osvaldo A. Rosso
This paper discusses the dynamics of intraday prices of twelve cryptocurrencies during last months' boom and bust. The importance of this study lies on the extended coverage of the cryptoworld, accounting for more than 90\% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.
Kripto parateknolojisi, insanların herhangi bir aracıya ihtiyaç duymadan dünyanın dört biryanında ödeme yapmalarını sağlar ve internet üzerinden çalışır. Bu teknolojininortaya çıkışı ile dikkatler bu paralara çevrilmiş, fiyatları hızla artmayabaşlamış ve aynı zamanda oynak hale gelmişlerdir. Bu çalışmada GARCH (1,1)modeli kullanılarak Bitcoin ve Litecoin piyasalarında haftanın günü ve yılınayı etkilerinin varlığı incelenmiştir. Çalışma dönemi 1 Mayıs 2013-21 Aralık2016 arasını kapsamaktadır. Elde edilen sonuçlar Bitcoin ve Litecoingetirilerinde haftanın günü ve yılın ayı etkilerinin var olduğunugöstermektedir. Pazartesi, Salı ve Cuma günlerinin Bitcoin getirileri üzerindepozitif ve anlamlı, Cumartesi’nin ise Litecoin getirileri üzerinde negatif veanlamlı etkisi olduğu tespit edilmiştir. Ayrıca, yılın ayı etkisi açısındanŞubat, Ekim ve Kasım aylarının Bitcoin üzerinde pozitif ve anlamlı, Litecoingetirilerinde ise Ağustos ayının negatif ve anlamlı etkisiolduğubelirlenmiştir.
Çalışmada gün geçtikçe popülerliği ve buna bağlı olarak toplam piyasa değerleri ve işlem hacimleri artan, çok sayıda ve çok çeşitli piyasalarda işlem gören kripto paraların fiyatlarının birbiri üzerindeki etkisi araştırılmıştır. Çalışmada, Bitcoin, Ethereum, Ripple, Bitcoin Cash, Cardano, Litecoin, NEM, NEO, Stellar ve IOTA kripto paralarının seçiminde toplam piyasa değerleri dikkate alınmıştır ve en yüksek toplam piyasa değerine sahip 10 kripto para analize dahil edilmiştir. 15 Aralık 2017 ve 17 Ocak 2018 tarihleri arasında çalışmaya konu olan kripto paraların günlük fiyat hareketleri arasındaki ilişkiyi incelemek için serilere Johansen Eşbütünleşme Testi ve Granger Nedensellik Testi uygulanmıştır. Çalışmanın sonucunda, Cardano’nun NEO’nun Granger nedeni olduğu, Bitcoin’in Bitcoin Cash’in Granger nedeni olduğu, Litecoin’in Bitcoin Cash’in Granger nedeni olduğu, NEM’in Bitcoin Cash’in Granger nedeni olduğu, Ripple’ın Bitcoin’in Granger nedeni olduğu, NEO ve Ethereum’un birbirinin Granger nedeni olduğu, NEO ve Litecoin’in birbirinin Granger nedeni olduğu ve NEM’in Stellar’ın Granger nedeni olduğu tespit edilerek, bu değişkenlerin fiyat hareketlerinin kısa dönemde birbirini etkilediği ortaya konmuştur.
This paper explores the predictive qualities of Bitcoin Miners Revenue on Bitcoin Returns. Using data on Bitcoin in the cryptocurrency market from July 1, 2010 to February 20, 2018, we reflect intervariable correlations not previously examined. We analyze those relationships with a conditional regression analysis adjusting for calendar effects. We separate the sample, and use the last 17 trading days (month) to test a strategy based on the probability of Bitcoin Returns moving higher. After a slight modification to the logistic regression analysis, we find a profitable trading strategy exists based solely on Bitcoin Miners Revenue and the probability of Bitcoin Returns moving higher.
We examine the stylized facts of eight forms of cryptocurrencies representing almost 70% of cryptocurrency market capitalization. In particular, the empirical results show that (1) there exists heavy tails for all the returns of cryptocurrencies; (2) the autocorrelations for returns decay quickly, while the autocorrelations for absolute returns decay slowly; (3) returns of cryptocurrencies display strong volatility clustering and leverage effects; (4) Hurst exponent for volatility is more volatile than that of the returns, while they all suggest the long-range dependence phenomena; and (5) there exists power-law correlation between price and volume.
Bu çalışmada her geçen gün ilgiyle izlenmeye devam edilen sanal para birimi Bitcoin’de çoklu balonların varlığı Phillips, Shi ve Yu (2015) tarafından geliştirilen GSADF birim kök testi ve kritik değerlerin tespitinde her türlü değişen varyans problemini hesaba katarak işlem yapan Harvey, Leybourne, Sollis ve Taylor (2016) tarafından geliştirilen metot takip edilerek araştırılmıştır. Veri seti 16.07.2010 ve 31.12.2017 tarihleri arasında günlük bazdaki 24 saatlik ortalama Bitcoin fiyatlarından oluşmaktadır. Yapılan analizler sonucunda söz konusu veri aralığının büyük bir kısmında Bitcoin fiyatlarında çoklu balonların varlığı görülmüştür
The commodity industry, the transaction lifecycle of commodities, its value chain and supply chain are complex systems with many parties involved. Ownership of cargoes are determined by who is holding the physical paper, the Bill of Lading. The participants in the industry require constant verification between parties, and this resultsin cumbersome paper-heavy back-office operations which are exposed to human errors. \nThe entire commodity transaction life-cycle involves the value chain and supply chain, and it creates a complicated and long chain involving several intermediaries, each taking a piece of the pie and adds to transaction costs for producers. The roles of intermediaries in the commodity industry can be financing trades, facilitating trade, managing risk, on-site inspection and verification of cargoes, shipping, and logistics.\nBlockchain technology is the technology underlying bitcoins and most of the cryptocurrencies in existence. Bitcoin enabled people to transfer money, peer-to-peer without an intermediary to establish trust and facilitate transactions. Bitcoin has paved the way for further use-cases of the technology, which has a much broader use-case spectrum than just being the underlying technology of cryptocurrencies.\nBlockchain is a decentralized, distributed ledger, where transactions are stored in blocks and secured with cryptography. It allows anyone to execute trade without an intermediary to establish trust between parties. It allows for one single source of the truth between counterparties through enhancing transparency, visibility and availability of transactions data and information.\nBlockchains can be fully transparent, but a blockchain can also allow for privacy. There are different types of blockchain, public/private/hybrid, and each of these types serves its purpose a little bit differently. In a private or hybrid blockchain, information that is sensitive to a certain trade remains private by only allowing the counterparties of that trade transparency into the transactions of that certain trade.\nThe blockchain technology enhances cyber security through decentralization and cryptography. Digital tokens can replace the Bill of Lading to track ownership of cargoes. Smart contracts that self-execute triggered upon a set of predetermined conditions are among the features of blockchain technology. The convenience of blockchain technology, is that anyone at any time can build its own decentralized application on top of already existing blockchain platforms like Ethereum, Hyperledger and others. These factors create for a unique opportunity to digitalize the commodity industry, gaining security, efficiency and opening up possibilities for new trade models in trade finance.
In the world finance and technological development in finance, along with innovative financial instruments, have attracted investors. The most popular of these developments is undoubtedly Bitcoin, which is an output of the blockchain infrastructure .Bitcoin that is not connected to a central authority and contains cryptographic features, is one of the crypto moneys. The fact that Bitcoin does not depend on Central Authority and disclose the factors affecting its price by supply and demand have resulted in high volatility. In this study, firstly blockchain technology will be explained briefly and time-dependent price estimates for Bitcoin which is one of the important outputs of this technology, will be made. Artificial Neural Networks (YSA), which has become increasingly popular among estimation methods in recent years, has been used in the study and compared with ARIMA in traditional estimation methods. The sample of the study was created using daily closing prices between 02.02.2012 - 09.01.2018 dates. As a result of this study, both directions and values of estimated prices by artificial neural networks MPL (6-3-1) model between 10.01.2018 - 18.01.2018 have been more successful than ARIMA (1.1.6) model.
This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.
This study back-tests a marginal cost of production model proposed to value\nthe digital currency bitcoin. Results from both conventional regression and\nvector autoregression (VAR) models show that the marginal cost of production\nplays an important role in explaining bitcoin prices, challenging recent\nallegations that bitcoins are essentially worthless. Even with markets pricing\nbitcoin in the thousands of dollars each, the valuation model seems robust. The\ndata show that a price bubble that began in the Fall of 2017 resolved itself in\nearly 2018, converging with the marginal cost model. This suggests that while\nbubbles may appear in the bitcoin market, prices will tend to this bound and\nnot collapse to zero.\n
Rohaifa Khaldi, Abdellatif El Afia, Raddouane Chiheb, Rdouan Faizi
The present study investigates the application of EEMD-ELMAN model to forecast the daily returns of the Bitcoin. More than seven years data were collected online from 18th July 2010 to 17th January 2018. Then the data signal was decomposed into several sub-signals using EEMD method. After, sub-signals were captured by different ELMAN models, and their output results were combined to generate the final forecast. Besides, the results of this study were compared against ELMAN and ARGARCH models. Hence, the statistical metrics revealed that the used model outperforms ELMAN network, and has approximately the same estimation error as ARGARCH, although the later model is prone to bad generalization due to the high gap between its approximation and generalization errors. Therefore, we can confirm that EEMD can be considered as a promising preprocessing technique, which enables to bring up the forecasting performance of ELMAN network with respect to highly volatile time series.
The realm of cryptocurrency has grown exponentially over the past decade, with the most rapid advances seen in the past few years as more and more parties around the world recognize the value of holding digital assets online. Statistics from Twitter support this statement where, approximately 1,500 Tweets about Bitcoin alone is recorded per hour. Consequently, many people are beginning to become more aware and accepting of the nature of digital currencies, and traders in particular seek to know how they can make profitable crypto-coin trades and investments. Although a number of research projects have been undertaken to develop systems that can effectively predict price movements in the cryptocurrency market, they display significant efficiency gaps, which this paper further explores. The authors then attempt to learn from past studies and construct a more holistic approach to a predictive price model for the cryptocurrency market. This focuses on assessing key factors that affect the volatility of the market - public perception, trading data, historic price data, and the interdependencies between Bitcoin and Altcoins - and how they can be best utilized from a technological aspect by applying sentiment analysis and machine learning techniques, to increase the efficiency of the process.
Cryptocurrencies are one of the most promising financial innovations of the last decade. Different from major stock indices and the commodities of gold and crude oil, the cryptocurrencies exhibit some characteristics of immature market assets, such as auto-correlated and non-stationary return series, higher volatility, and higher tail risks measured by conditional Value at Risk (VaR) and conditional expected shortfall (ES). Using an extreme-value-theory-based method, we evaluate the extreme characteristics of seven representative cryptocurrencies during 08 August 2015–01 August 2017. We find that during the sub-period of 01 August 2016–01 August 2017, there are finite loss boundaries for most of the selected cryptocurrencies, which are similar to the commodities, and different from the stock indices. Meanwhile, we find that left tail correlations are much stronger than right tail correlations among the cryptocurrencies, and tail correlations increased after August 2016, suggesting high and growing systematic extreme risks. We also find that cryptocurrencies to be both left tail independent, and cross tail independent with four selected stock indices, which implies part of the safe-haven function of the cryptocurrencies, indicating their ability to be a great diversifier for the stock market as gold, but not enough to be a tail hedging tool like gold.
Mutlu Başaran Öztürk, Halil Arslan, Temur Kayhan, Mustafa Uysal
2017 yılında Bitcoin’in piyasa değerinde önemli bir artış yaşanmış ve 200 milyar dolar seviyesi aşılarak Bitcoin kurumsal yatırımcıların gündemine gelmeye başlamıştır. CME ve CBOE gibi dünyanın en büyük vadeli işlem borsaları Bitcoin’i listelerken Microsoft, PWC ve Overstock gibi kurumlar Bitcoin’i tanımlamaya başlamışlardır. Bitcoin’in bir yatırım aracı olarak görülebilmesi için bazı şartlar gereklidir. Verimli bir piyasada işlem görmesi, fiyatlama formasyonunun belirginleşmesi ve portföyler için bir çeşitlendirme aracı olabilmesi bunlardan bazıları olarak görülebilir. Ana akım varlık grupları ile Bitcoin arasındaki uzun vadeli ilişkiyi Johansen Eşbütünleşme testi ile inceleyen çalışma sonuçlarına göre Bitcoin’in altın haricinde diğer geleneksel finansal ve emtia varlıklarından bağımsız bir hareket gösterdiği ortaya çıkmıştır. Bitcoin’in söz konusu bağımsız hareketi Bitconomi olarak tanımlanırken bu durum mikro seviyede riskli bir varlığın makro anlamda portföylerin riskini düşürebileceği anlamına gelmektedir. Finansal sistemde çok küçük bir alanı işgal etmesi ve Bitcoin üretimindeki zorluk derecesinin klasik ekonomi ile çelişmesi korelasyonun anlamsız olmasının nedenleri arasında gösterilebilir. Kuzey Kore ve Ukrayna gerilimlerinde Bitcoin fiyatındaki artışlar ve altın ile Bitcoin arasındaki uzun vadeli pozitif ilişki yüksek varyansı nedeniyle eleştirilen Bitcoin’in gelecekte güvenli liman olabileceği gibi ilginç bir ironiye işaret etmektedir. Literatürdeki çalışmalar her geçen yıl Bitcoin’in varyansının gerilediğini göstermektedir.
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.