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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.
The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%
Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.
This study investigates the asymmetric causal relations between Bitcoin and gold, Brent oil, US dollar, S&P 500 and BIST 100 Indexes for the weekly data of the period between November 2013 and July 2018 via by Hatemi-J (2012) test. The results indicate only a causal link going from the Bitcoin price to S&P 500 Index. Consequently, a change in Bitcoin prices appears to influence the investors’ decisions on the S&P 500 Index. Therefore, it can be said that the investors in S&P 500 Index have closely followed the new macro-financial developments in the market and have been active on the S&P 500 market. However, the presence of a causality relation between Bitcoin price and other variables cannot be determined. Thus, it is supposed that Bitcoin may exist in association with the commodity market and other global indicators in the future, along with the recognition of the Bitcoin currency by countries, its being accepted as a means of exchange and its increased reliability.
There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability. In this article we examine whether bitcoin returns are predictable by a large set of bitcoin price-based technical indicators. Specifically, we construct a classification tree-based model for return prediction using 124 technical indicators. We provide evidence that the proposed model has strong out-of-sample predictive power for narrow ranges of daily returns on bitcoin. This finding indicates that using big data and technical analysis can help predict bitcoin returns that are hardly driven by fundamentals.
Boris Radovanov, Aleksandra Marcikić, Nebojša Gvozdenović
Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.
Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s \namorphous nature: is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and \nthe digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency \ncontrols and acquire US dollars. For the period August 2010 to February 2018, we examine the components of Bitcoin mining \nrevenues, their statistical contribution to daily changes, and to its variance. We provide evidence that Bitcoin transaction processing \nis capacity constrained.