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4,843 results · page 185 of 202
Julián Andrada Félix, Adrián Fernández-Pérez, Simón Sosvilla‐Rivero
No abstract is available for this record.
Saketh Aleti, Bruce Mizrach
Abstract We study Bitcoin (BTC) trading at the Chicago Mercantile Exchange (CME) and four settlement spot exchanges that transact $146 million per day in the BTC/USD pair. Spot market median trade sizes are under $1,300 but exceed $18,000 on the CME. Bid‐ask spreads average 0.0298%. Trade sizes of over $1 million move markets by less than 1%. 2.5% of trades and 15.5% of cancellations on Coinbase take place within 50 ms. Bid‐ask spreads exceed 0.8% for only 226 s. Most executions trade‐through better quotes, with estimated losses of $36 million. The CME leads price discovery. BTC leads Ethereum price adjustment.
Sana Guizani, Ines Kahloul Nafti
The emergence of Bitcoin (BTC) has triggered intense discussions. Despite the particular interest of the public, the theoretical understanding of the value of this crypto currency is limited. This is why current research is trying to find better leads to evaluate a complex phenomenon: the BTC price. The volatility of its price presents a certain specificity compared to the traditional currencies. In order to understand the reasons for this volatility, we try to identify and to analyze the main determinants of the BTC price and to estimate their influence. We apply time series to daily data for the period from 19/12/2011 to 06/02/2018. We used several approaches, including the Auto Regressive Distributed Lag ARDL model, the cointegration test at Pesaran et al. (2001) and the Granger causality test in the sense of Toda and Yamamoto (1995). Our estimated results suggest that the number of addresses, the attractiveness indicator and the mining difficulty have a significant impact on the BTC price with variations over time. On the other hand, the transaction volume, the stock, the EUR/USD exchange rate and the macroeconomic and financial development do not determine the price of the BTC in the short term as well as in the long term.
Leopoldo Catania, Mads Sandholdt
This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.
Carlos Trucíos, Aviral Kumar Tiwari, Faisal Alqahtani
Risk management is an important and helpful process for investors, hedge funds, traders and market makers. One of its key points is the appropriate estimation of risk measures which can improve the investment decisions and trading strategies. The high volatility of cryptocurrencies turns them a really risky investment and consequently, appropriate risk measures estimation is extremely necessary. In this article, we deal with the estimation of two widely used risk measures such as Value-at-Risk and Expected Shortfall in a cryptocurrency context. To face the presence of outliers and the correlation between cryptocurrencies, we propose a methodology based on vine copulas and robust volatility models. Our procedure is illustrated in a seven-dimensional equal-weight cryptocurrency portfolio and displays good performance.
Canh Phuc Nguyen, Nguyen Quang Binh, Thanh Dinh Su
The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.
Vasily Derbentsev, Natalia Datsenko, Olga Stepanenko, Vitaly Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
A. Can Inci, Rachel Lagasse
Purpose - This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios. Design/methodology/approach - The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios. Findings - The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios. Originality/value - This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.
Paraskevi Katsiampa, Shaen Corbet, Brian M. Lucey
Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.
Samuel Asante Gyamerah
Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.
Olivier Darné, Amélie Charles
International audience
Dehua Shen, Andrew Urquhart, Pengfei Wang
Abstract This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.
Ladislav Krištoufek
Bitcoin as a major cryptocurrency has come up as a shooting star of the 2017 and 2018 headlines. After exploding its price twenty times just in the twelve months of 2017, the tone has changed dramatically in 2018 after major price corrections and increasing concerns about its mining power consumption and overall sustainability. The dynamics and interaction between Bitcoin price and its mining costs have become of major interest. Here we show that these two quantities are tightly interconnected and they tend to a common long-term equilibrium. Mining costs adjust to the cryptocurrency price with the adjustment time of several months up to a year. Current developments suggest that we have arrived at a new era of Bitcoin mining where marginal (electricity) costs and mining efficiency play the prime role. Presented results open new avenues towards interpreting past and predicting future developments of the Bitcoin mining framework.
Sang Hoon Kang, Ron McIver, José Arreola Hernández
In this paper, we use dynamic conditional correlations (DCCs) and wavelet coherence to examine the hedging and diversification properties of gold futures vis-à-vis Bitcoin prices. Our research aims to reveal whether the bubble patterns of behavior in gold futures prices can be used to hedge against the bubble behavior in the Bitcoin market in the short-term, and vice versa; as well as whether each can be used to manage and hedge overall market and sector downside risk of the other asset/commodity. We find evidence of volatility persistence, causality, and phase differences between Bitcoin and gold futures prices. Contagion is observed to increase during the European sovereign debt crisis. Wavelet coherence results indicate a relatively high degree of co-movement across the 8–16 weeks frequency band between Bitcoin and gold futures prices for the 2012–2015 time period.
Anders Stensås, Magnus Frostholm Nygaard, Khine Kyaw, Sirimon Treepongkaruna
This paper investigates whether Bitcoin acts as a diversifier, hedge or safe haven tool for investors in major developed and developing markets, as well as for commodities. This paper employs the GARCH Dynamic Conditional Correlation (DCC) model. The sample covers seven developed and six developing countries, five regional indices and 10 commodity series. The results show that Bitcoin acts as a hedge for investors in most of the developing countries such as Brazil, Russia, India and South Korea, but only as a diversifier for investors in developed countries and for commodities. Moreover, Bitcoin acts as a diversifier for all the 10 commodities studied here. During the US election in 2016, Brexit referendum in 2016, and the burst of Chinese market bubble in 2015, Bitcoin acted as a safe haven asset for both the US and non-US investors. Understanding the role of Bitcoin is important for financial market participants who seek protection against market turmoil and downward movements. Furthermore, our findings would be of interests to regulators and governments to engage in more discussion of the role of Bitcoin in financial markets. This paper contributes to the ongoing debate on the usefulness of Bitcoin for investments. Furthermore, it distinguishes the benefits of Bitcoin as a diversifier, hedge and safe haven to investors in the developed versus developing markets.
Dionisis Philippas, Hatem Rjiba, Khaled Guesmi, Stéphane Goutte
No abstract is available for this record.
Dirk G. Baur, Lai T. Hoang
No abstract is available for this record.
Eyyüp Ensari Şahin, Oktay Özkan
Bitcoin, merkezi bir otoriteye veya finansal bir kuruluşa bağlı olmayan ve kriptografik özellikler içeren dijital (kripto) paralardan biridir. Bitcoin’ in Merkezi otoriteye bağlı olmaması ve fiyatını etkileyen faktörlerin arz ve talep ile açıklanması yüksek volatite ile sonuçlanmıştır. Son dönemlerde yatırımcıların en büyük endişesi fiyatlardaki aşırı volatilite durumudur. Çalışmada Blockchain Teknolojisi, Madencilik ve Blockchain Teknolojisinin bir çıktısı olan Bitcoin kısaca anlatılmıştır. Çalışmanın uygulama bölümünde literatürde sıklıkla kullanılan yöntemlerden olan ve asimetrik volatilitenin belirlenmesi amacıyla ARCH, GARCH, ARCHM, EGARCH ve TARCH modelleri kullanılmıştır. Bu amaçla Bitcoin/USD kuru kapanış fiyatlarından Bitcoine ilişkin tarihsel getiriler hesaplanmıştır. Hesaplama dönemi 01.01.2015-11.02.2018 olarak belirlenmiştir. Yapılan analizler sonucunda volatilite tahmini için en iyi sonuç veren TARCH yöntemi bulunmuştur.
Özge Korkmaz
Abstract. This study focuses on the dollar, euro, gold, bitcoin and the impact of bubbles in financial investment instruments on bitcoin returns in the context of Turkey. The causal relationships (using the Toda-Yamamato causality test) between the returns of these financial instruments were also determined. In performing this assessment, the sup augmented Dickey-Fuller (SADF) and generalised SADF (GSADF) tests were employed to determine the existence of bubbles based on the period from 1 August 2018 to 23 March 2018. The volatility of bitcoin was tested by autoregressive conditional variant models. As aresult, it was shown that the observed bubbles in gold’s, the euro’s and the dollar’s returns reduced the volatility of bitcoin’s returns. Then, it was shown that the dollar’s, the euro’s and gold’s returns affected bitcoin’s returns. Keywords. Speculative bubbles, Bitcoin, Investment instruments, Autoregressive conditional heteroskedasticity models, Toda-Yamamato causality. JEL. G10, C58, E44.
Mustafa Çıkrıkçı, Mustafa Özyeşil
The main purpose of this study in determine whether Bitcoin is becoming an alternative investment option compared to other financial instruments in Turkey. To perform analysis for this purpose, we created sample includes Bitcoin, Bist 100 National Index, Bond, Euro and Gold. We calculated daily returns in terms of % for each type of instrument for the period range from 02.02.2012 to 17.12.2018. Methodology -In the study, we tested the stationarity of the series and the causality relationships between the series through the nonlinear unit root and causality tests. Stationarity of the series and causality relations between them are determined with the help of charts. Both in unit root test and in causality test, firstly we obtained test statistics and normalized them by using critical values then transferred them to the charts. In order to decide about when test statistics is higher than critical values null hypothesis is rejected. Findings-Bitcoin, Usd, Gold and Bond are found non-stationary for the whole period. We find out that returns of Euro is more stable than Usd. Bitcoin has been fluctuating during all period except for first months of 2013 while Gold's returns are intensively volatile when politic and economic risks are increasing. This show investors in Turkey still consider Gold as safe port for their investments. Usd currency is volatile for all period because of its increasing demand all over the world. In causality test we observed that there is a causality relation from Bitcoin's returns to Bist 100 returns. This result shows that Bitcoin is becoming an alternative (substitute) investment tool for domestic and foreign investors compared to Borsa Istanbul. There is more causality relation between Bitcoin and Gold than Bitcoin and Bist 100. Conclusion-Based on the findings of this analysis, , it may be accepted that Bitcoin has been becoming an alternative investment/savings tool for the Turkey case. Regulatory institutions should create a required legal framework for the Bitcoin because Bitcoin has a great potential to prevent of tax evasion, the terminate the informal economy and eliminate intermediation costs.
Hakan YILDIRIM
Son yılların önemli araştırma konusu olan kripto paralar içinde en önemlisi olan Bitcoin bir yıl içinde yirmi katın üzerinde değer kazandı ve yatırımcılar açısından güven duyulan bir para birimi haline geldi. Artan güven ile birlikte Bitcoin yatırımcılarının da çoğalmasıyla piyasadaki hacimde gözle görülür bir büyüme meydana geldi. Bu sayede Euro ve Amerikan Doları gibi para birimlerinin 2008 krizinden sonraki güvensiz hale gelmesi Bitcoin’in güven duyulan bir para birimi haline gelmesine sebep oldu. Sadece para birimleri değil menkul kıymet ve emtia gibi yatırım araçlarına da alternatif olan Bitcoin yüzyıllardır bireysel yatırımcıların vazgeçilmez yatırım aracı olan altının konumunu neredeyse alır vaziyete geldi. Söz konusu çalışma ani şekilde yükselişe geçen ve yatırımcılar tarafından hızlı bir şekilde yatırım aracı olarak kabul edilen Bitcoini, asırlardır önemli bir yatırım aracı olarak kabul edilen altına karşı ADF Birim Kök Testleri, Johansen Koentegrasyon Testi, Hata Düzeltme Modeli ve Düzeltilmiş En Küçük Kareler Modeli kullanılarak değerlendirilmektir.
David K. Leonard, Horst Treiblmaier
No abstract is available for this record.
İbrahim Çütçü, Yunus Kılıç
Dijital para birimleri son yıllarda etkinliğini artırarak uluslararası piyasalarda önemli oranda talep görmeye başlamıştır. Dijital para birimleri arasında işlem hacmi ve getiri oranları dikkate alındığında, Bitcoin ön plana çıkmaktadır. Çalışmada, Bitcoin fiyatları ile döviz kurları arasındaki ilişki incelenmektedir. Bu doğrultuda kurulan model kapsamında, 24 Kasım 2013 - 04 Mart 2018 dönemlerini kapsayan haftalık veriler ile yapısal kırılmalı testler kullanılarak döviz kurları ile Bitcoin fiyatları arasındaki ilişki irdelenmiştir. Analizlerde kullanılan verilerin I (1) düzeyinde durağan olduğu tespit edilmiş olup yapısal kırılmaya izin veren Maki Eşbütünleşme testi sonuçlarına göre değişkenler arasında yapısal kırılmalarla birlikte uzun dönemli bir ilişki olduğu sonucuna ulaşılmıştır. Hacker-Hatemi-J Bootstrap Nedensellik testi sonuçlarında ise dolar kurundan Bitcoin fiyatlarına doğru %1 anlamlılık düzeyinde nedensellik ilişkisi tespit edilmiştir.
Sashikanta Khuntia, J. K. Pattanayak
No abstract is available for this record.