Cryptocurrencies represent a new type of digital asset that cannot be linked to the framework of fundamental and systematic factors of existing financial instruments of the traditional capital market. Due to the lack of strictly defined fundamental indicators, supported by the results of research by the academic community, considering cryptocurrencies as investment opportunities can put investors in a subordinate position, a situation of complete uncertainty. Cryptocurrencies and their entire technical infrastructure are still a kind of unknown to the general public. Due to this, but also the lack of a regulatory framework, investors have to rely on sometimes uncertain information gathered through various media platforms. However, regardless of the type of assets and the mentioned shortcomings, when constructing a portfolio, investors should consider the dynamics of returns of potential components of the portfolio in order to identify and quantify the assumed investment risk and define the expected return. Cryptocurrencies are based on the idea of decentralization initially introduced by bitcoin blockchain technology and as such have their own historical sequence of origin. Since bitcoin is the first digital currency based on asymmetric cryptography, the change in its value can serve as a leading indicator of the movement of the cryptocurrency market as a whole. Accordingly, this paper will formally identify and describe the performance of the cryptocurrency portfolio with different optimization goals taking into account the assumption of a significant systematic impact of bitcoin cryptocurrency on the dynamics of the value of the aggregate secondary cryptocurrency market. For this purpose, six optimization targets will be formed: MinVar, MinCVaR, MaxSR, MaxSTARR, MaxUT and MaxMean. The results of the formed portfolios will be compared with the results of portfolios with the same allocation objectives, but which include a limitation on the impact of BTC as a systematic factor. The results suggest that by controlling the exposure by factor, better overall portfolio performance can be achieved through higher returns and Sharpe Ratio in four of the six implemented optimization strategies, while in terms of absolute risk measure five out of six portfolios achieved lower overall risk. Also, the obtained results confirm that the bitcoin transaction system plays a major role in defining the future movement of the value of the secondary cryptocurrency market.
<title>Abstract</title> <bold>Background: </bold>Cryptocurrencies, especially Bitcoin, has become popular for investors in recent years. The volatility of bitcoin and time horizon are the center point for investment decisions. However, attention is not often drawn to the relationship between bitcoin and equity indices. This study investigates the volatility and time frequency domain of bitcoin among five Asean countries through a rich database which covers daily data from July 2010 until April 2019.<bold>Methods: </bold>Advanced econometrics and Wavelets Cross-Coherence Spectrograms, this study investigates the existence of long run association between bitcoin and the studied market indices. M-GARCH analysis is been employed to investigate the unconditional volatility of market indices and Bitcoin.<bold>Results: </bold>The findings present the long run association<bold> </bold>with positive (Philippines) and negative (Japan, Korea, Singapore, Hong Kong) relations. Moreover, only one market (KOREA) shows a short run association with bitcoin. The M-GARCH analysis reveals, most of the selected Asean countries have a low unconditional volatility with bitcoin. Except for Philippines in which the co-movement is average, Wavelet analysis reveals the presence of a strong and long co-movements for most of the selected Asean countries with bitcoin.<bold>Conclusions: </bold>Most of our results are consistent and illustrate different dimensions of long and short run relationship, volatilities, correlations, and time-frequency analysis. This study utilized Asean emerging economies which are rarely available in the literature as existing studies are more skewed towards the West. We believe the outcomes of this study will be a significant for industry practitioners (i.e., retail and institutional investors) on designing better strategies to diversify the stock portfolio with different holding period horizons and dimensions.
Huaigang Long, Adam Zaremba, Ender Demir, Jan Jakub Szczygielski · 5 authors
This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.
Marina Resta, Paolo Pagnottoni, Maria Elena De Giuli
In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.
Türker Teker, Ayşen Konuşkan, Vesile Ömürbek, İsmail BEKÇİ
Bitcoin, kripto paralar içinde, günlük işlem hacmi en yüksek olan kripto para birimi olarak ön plana çıkmaktadır. Bu çalışmada, hem bitcoin hem de kripto paralar hakkında küresel düzeyde yayınlanmış olan olumlu ve olumsuz haberlerin, bitcoin gün sonu kapanış fiyatları, gün içi bitcoin en yüksek fiyat seviyesi ve günlük bitcoin işlem hacimlerinde yarattığı değişim incelenmiştir. Çalışmada Mayıs 2018-Aralık 2018 arasında yahoofinance.com ve Bloomberg.com web sayfalarında bitcoin ve kripto paralar ile ilgili yayınlanan haberler değerlendirmeye alınmıştır. Çalışmadan elde edilen bulgular, kripto paralar ve bitcoin ile ilgili olumlu ve olumsuz çıkan haberlerin, bitcoin fiyatları ve işlem hacimleri üzerinde bir farklılaşmaya yol açmadığını ortaya koymaktadır.
This paper sets out to explore whether Bitcoin can be considered as a globally accepted asset that has a resemblance to gold, which is widely considered to be the safest choice. An integrated overview of the empirical findings generated by the nascent but increasingly proliferating literature concerning the nexus between Bitcoin and gold is provided. The majority of evidence reveals that Bitcoin has a long way to go before it acquires the same characteristics as the safe-haven asset of gold. Overall, Bitcoin is found to be an efficient hedge against oil and stock market indices, but to a lesser extent than gold. Bitcoin presents low or negative correlations or an asymmetric non-linear linkage with gold. Despite sharing some common features with traditional assets, Bitcoin is found to be a good hedging asset in portfolios with gold. Moreover, evidence reveals that gold is a better and more stable safe-haven investment than Bitcoin.
The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.
Beata Szetela, Grzegorz Mentel, Urszula Mentel, Yuriy Bilan
The crypto exchanges operate primarily on the internet, where the speed of information spreading is significant. Therefore, it is expected that there should be no significant differences among the individual exchanges concerning the same asset being traded. Prices should quickly reach comparable values on all stock exchanges, and they should return to equilibrium in a relative time frame. Hence, the investors, while making decisions on the selection of a cryptocurrency market, should be guided primarily by the exchange security considerations, its flexibility, availability of a product offer, and costs of order processing. The work aims to check whether virtual currency exchanges differ from each other in the context of directional movement, both in an upward and downward trend. To achieve the objective of the paper, we used Directional Movement Index, supported by the Directional Indicators, to compare the distribution of the strength of the directional movement across three different cryptocurrency exchanges (Bitstamp, Coinbase, Kraken) within the up and the downward price movement phase. The comparison is made based on the results of the non-parametrical tests such as Wilcoxon test, Hodges Lehmann test, Ansari-Bradley test, and Conover test. The results show that theoretically, the choice of a cryptocurrency exchange in an upward trend will cause no significant difference for an investor and its strategy. However, the choice of a stock exchange in a downward trend may have a substantial impact on the rates of return.
Purpose The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the blockchain index is necessary. Design/methodology/approach This paper uses the Granger causality test to explore the correlation between bitcoin and the blockchain index. Furthermore, their volatility is analyzed by a GARCH-class model. Findings The results show that no significant correlation exists between bitcoin and the blockchain index; external shocks aggravate the volatility of bitcoin and the blockchain index, and the volatility has a certain degree of sustainability; and blockchain index has obvious leverage, namely, its decline has a stronger impact. Originality/value The volatility of bitcoin and the blockchain index is crucial for investors.
Nowadays, accurate prediction of cryptocurrency price variation based on their important role in the world economy is an important and challenging issue. In this study, various parameters that affect the cryptocurrency value have been considered. For the first phase, four major price features of digital currencies have been analysed to determine the effect of each feature on the volatility prediction of future days. This study aims to understand and identify daily trends in the cryptocurrency market while gaining insight into optimal features surrounding their price. For the second phase, the price variation has been predicted with the highest possible accuracy with a new intelligent method. The proposed method consists of a neural network‐based prediction algorithm and particle swarm optimisation. The obtained results show the capbility of the proposed method.
Roy Cerqueti, Massimiliano Giacalone, Raffaele Mattera
Recently, cryptocurrencies have attracted a growing interest from investors,\npractitioners and researchers. Nevertheless, few studies have focused on the\npredictability of them. In this paper we propose a new and comprehensive study\nabout cryptocurrency market, evaluating the forecasting performance for three\nof the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in\nterms of market capitalization. At this aim, we consider non-Gaussian GARCH\nvolatility models, which form a class of stochastic recursive systems commonly\nadopted for financial predictions. Results show that the best specification and\nforecasting accuracy are achieved under the Skewed Generalized Error\nDistribution when Bitcoin/USD and Litecoin/USD exchange rates are considered,\nwhile the best performances are obtained for skewed Distribution in the case of\nEthereum/USD exchange rate. The obtain findings state the effectiveness -- in\nterms of prediction performance -- of relaxing the normality assumption and\nconsidering skewed distributions.\n
Abstract This study explores whether Bitcoin constitutes as a hedging instrument whilst seeking portfolio diversification opportunities among sustainable, conventional and Islamic asset classes since Bitcoin emerges as a distinct alternative investment and asset class across the world. We apply multivariate generalised autoregressive conditional heteroscedastic‐dynamic conditional correlation and continuous wavelet transforms based on the recent data set ranging from August 18, 2011, to September 10, 2018. First, our findings show that Bitcoin returns are mean‐reverting which implies that its value tends to come down to mean value in the long run and not completely crushed to zero irrespective of price changes suggesting Bitcoin as a sustainable asset class. Second, the time‐invariant model shows that Bitcoin offers portfolio diversification opportunities with almost all equity indices, in particular, Dow Jones Islamic followed by FTSE 4 Good index. Finally, the time‐variant analysis reconfirms that Bitcoin offers portfolio diversification benefits both in the short and long run. These findings carry meaningful policy considerations for fund managers and cross‐country investors.
The main goal of this study is to examine whether the cryptocurrency market impacts the stock market returns in the Gulf countries. Understanding this impact is quite interesting to clarify whether the cryptocurrency market and the stock market are substitutes or complements for investors. The author compiles the data on the stock market of the Gulf countries with the cryptocurrency data on a daily basis over the period 2014-2019. Generalized Method of Moments with Instrumental Variable (IV - GMM) approach has been implemented as the main strategy to fulfill the objective of the paper. The results of this paper show that the Stock market and the cryptocurrency market are substitutes for investors in Gulf countries. In fact, each 10 percent increase in the cryptocurrency returns is associated with a decline in the stock market returns by 0.17 percent. The cryptocurrency market hampers the stock market indices in the Gulf countries. Having agreed upon in the literature that the stock market is affected by fundamental factors, market sentiment, technical factors, and anomalies, this study offers robust evidence that the cryptocurrency should be introduced as one of the main determinants of stock market prices and returns.
Ida Musiałkowska, Agata Kliber, Katarzyna Świerczyńska, Paweł Marszałek
Purpose This paper aims to find, which of the assets: gold, oil or bitcoin can be considered a safe-haven for investors in a crisis-driven Venezuela. The authors look also at the governmental change of approach towards the use and mining of cryptocurrencies being one of the assets and potential applications of bitcoin as (quasi) money. Design/methodology/approach The authors collected the daily data (a period from 01 May 2014 to 31 July 2018) on the development of the following magnitudes: Caracas Stock Exchange main index: Índice Bursátil de Capitalisación (IBC) index; gold price in US dollars, the oil price in US dollars and Bitcoin price in bolivar fuerte (VEF) (LocalBitcoins). The authors estimated a threshold VAR model between IBC and each of the possible safe-haven assets, where the trigger variable was the IBC; then the authors modelled the residuals from the TVAR model using MGARCH model with dynamic conditional correlation. Findings The results show that that gold is a better safe-haven than oil for Venezuelan investors, while bitcoin can be considered a weak safe haven. Still, bitcoin can perform (to a certain extent) money functions in a crisis-driven country. Research limitations/implications Further research after the change of local currency from VEF into bolivar soberano might be looked at on the later stage. Practical implications The authors provide evidence on which of analysed asset is the best safe-haven for the investors acting in the time of the crisis. The evidence goes in line with other authors’ findings, thus, the results might bring implications for investors of more universal character. Additionally, the result might be helpful for governments and/or monetary authorities while projecting institutional frameworks and conducting monetary policy. Social implications The unprecedented economic crisis in Venezuela was one of the factors that fuelled the mining and use of cryptocurrencies in the daily life of its citizens. Nowadays, the country is a leader in terms of the use of bitcoin and other cryptocurrencies in Latin America. The results show a potential application of bitcoin as a store of value or even means of payments in Venezuelan (or in other countries affected by the crisis). Originality/value The paper builds on the original data set collected by the authors and brings evidence from the models the authors constructed to verify, which asset is the best option for investors in hard times of the crisis. The authors add to the existing literature on financial assets, cryptocurrencies and behaviour of investors under different economic conditions.
Blockchain teknolojisinin aracısız veri/para transferi gerçekleştirmesi ile tüm çevrelerin gündemine gelen Bitcoin, birçok yatırımcının ilgi odağı olmuş ve Bitcoin ’in popüler olması ile birçok kripto para piyasaya sürülmüştür. Kripto paralarda fiyat volatilitesinin yüksekliği hızlı para kazanma arzusu içinde olan ve risk iştahı yüksek olan yatırımcıları fiyat tahminlemesi ve fiyatları etkileyen değişkenlerin belirlenmesi noktasında analiz yapmaya itmiştir. Bu çalışmanın amacı Aralık 2017 itibari ile değeri yaklaşık 20.000 ABD Dolara ulaşan ve yüksek volatilitesi ile yatırımcıların sürekli gündeminde olan Bitcoin fiyatına etki eden faktörlerin belirlenmesidir. Bu amaçla çalışmada literatürde kullanılan değişkenlere (Altın ve ABD Dolar) ek olarak küresel risklerin (Finansal Baskı Endeksi ve Jeopolitik Risk Endeksi) etkisi de ölçülmeye çalışılmıştır. Çalışama da Bitcoin fiyatı üzerine etki etmesi muhtemel değişkenler Çok Değişkenli Uyarlanabilir Regresyon Uzanımları-MARS yöntemi ile analiz edilmiştir. Çalışmada kullanılan veriler 2012/1-2019/11 yılları arasında aylık verilerden oluşmaktadır. Çalışmanın sonucunda kullanılan tüm bağımsız değişkenlerin belirli şartlar altında Bitcoin fiyatına etki edebileceği sonucuna ulaşılmıştır.
Abstract The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency. Using the error correction model, the relationship between the price of the virtual currency, Bitcoin, and the number of Google searches that used the terms bitcoin , bitcoin crash and crisis between December 2012 and February 2018 is analyzed. The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period. The Johansen (J Econ Dyn Control 12:231-254, 1988) test demonstrates that the price and number of searches on Google for the first two terms are cointegrated. This research indicates that there are strong short-term and long-term dynamics among attractiveness factors, suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase. In contrast, an increase in market mistrust over a collapse of the currency, as measured by the term bitcoin crash , is followed by a fall in price. Intense world economic crisis events appear to have a strong impact on interest in the virtual currency. This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment, increasing its price. Based on it, bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.
Cryptocurrencies have recently captured the interest of the econometric literature, with several works trying to address the existence of bubbles in the price dynamics of Bitcoins and other cryptoassets. Extremely rapid price accelerations, often referred to as explosive behaviors, followed by drastic drops pose high risks to investors. From a risk management perspective, testing the explosiveness of individual cryptocurrency time series is not the only crucial issue. Investigating co-explosivity in the cryptoassets, i.e., whether explosivity in one cryptocurrency leads to explosivity in other cryptocurrencies, allows indeed to take into account possible shock propagation channels and improve the prediction of market collapses. To this aim, our paper investigates the relationships between the explosive behaviors of cryptocurrencies through a unit root testing approach.
Roy Cerqueti, Massimiliano Giacalone, Raffaele Mattera
Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market capitalization. At this aim, we consider non-Gaussian GARCH volatility models, which form a class of stochastic recursive systems commonly adopted for financial predictions. Results show that the best specification and forecasting accuracy are achieved under the Skewed Generalized Error Distribution when Bitcoin/USD and Litecoin/USD exchange rates are considered, while the best performances are obtained for skewed Distribution in the case of Ethereum/USD exchange rate. The obtain findings state the effectiveness -- in terms of prediction performance -- of relaxing the normality assumption and considering skewed distributions.
Anwar Hasan Abdullah Othman, Syed Musa Alhabshi, Salina Kassim, Adam Abdullah · 5 authors
Purpose This study uses the autoregressive distributed lag model (ARDL) econometric approach to investigate empirically the effects of cryptocurrencies, the gold standard and traditional fiat money on global income inequality measured based on the Gini coefficient, and various ratios of income inequality distribution such as top 1 per cent, top 10 per cent, top 40 per cent and top 50 per cent. Design/methodology/approach The study uses the ARDL econometric approach. Findings The findings indicated that cryptocurrency and gold standard monetary systems contributed significantly to reducing global inequality of income and wealth distribution. Conversely, the traditional fiat money system contributes positively to global income and wealth inequality while also contributing significantly to their fluctuation. Practical implications This suggests that the fiat monetary system results in the coercive redistribution of income and wealth if governments pursue a social welfare policy. They must resolve this conflict between the current fiat monetary system and social policy by opting for an alternative monetary system such as cryptocurrency or gold standard. These alternative monetary systems offer the promise of resolving the income and wealth inequality associated with the traditional monetary system which are accompanied with the channels of inflation, lack of financial inclusion and debt creation, and to offer a more sustainable financial system. Originality/value The study recommends that monetary policy must be revisited to account for its direct effect on income and wealth redistribution to achieve social welfare goals.
Bitcoin is the digital currency of the digital economy. This article is an attempt to reveal the effects of policy uncertainty on Bitcoin returns with economic policy uncertainty (EPU) in the US, the UK, Japan , China, and Hong Kong . Furthermore, we also present the results of monetary policy uncertainty (MPU) on the Bitcoin market. The robust estimations from the quantile regression and Markov regime-switching model show that Bitcoin returns are affected by EPU. One of the essential findings is that Bitcoin returns are more responsive to EPU in the US, China, and Japan. In the US and Japan, uncertainty has a negative effect on the Bitcoin market whereas in China it has a positive effect. Global MPU uncertainty is also significant in explaining Bitcoin exchange rates. Moreover, the Bitcoin market is negatively affected by uncertainty in Federal Open Market Committee (FOMC), the gross domestic product, and other macroeconomic data. Uncertainty in the equity market and Bitcoin returns are negatively associated.
In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the problem, but none without shortcomings. We combine an approach that uses historical values of returns and their volatilities - GARCH family of models, with a so-called "Mixture of Distribution Hypothesis", which states that the dynamics of price returns are governed by the information flow about the market. Using time-series of Bitcoin-related tweets and volume of transactions as external information, we test for improvement in volatility prediction of several GARCH model variants on a minute level Bitcoin price time series. Statistical tests show that the simplest GARCH(1,1) reacts the best to the addition of external signal to model volatility process on out-of-sample data.
F. N. M. de Sousa Filho, J. N. Silva, Mário Augusto Bertella, Edgardo Brigatti
In this paper, we explore some stylized facts in the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2018. Despite Bitcoin presents some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying asset or benchmark, a clear asymmetry between demand and supply and the presence of inefficiency in the form of very strong arbitrage opportunity, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.