Pairs trading is a popular quantitative trading strategy with the advantage of a similarity in price movement to financial assets. Assuming that the price spreads of trading pairs are mean-reverting, this strategy exploits the disequilibrium in financial markets to find arbitrage investment opportunities. Pairs trading has been widely applied to stock, ETF, and commodity markets. However, the effectiveness of this method for cryptocurrency markets has yet to be properly explored. Therefore, we examine the profitability of pairs trading for 26 cryptocurrencies traded on the Binance exchange at high frequencies of 1, 5, and 60 min. In addition to the traditional statistical methods of distance, correlation, cointegration, and stochastic differential residual (SDR), we focus on two evolutionary algorithms: genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). During the 79-trading-day period from 11 January to 31 March 2018, NSGA-II showed the best results at all frequencies, with an average return of 2.84%. Among the statistical models, SDR ranks first, whereas Correlation ranks last, with average returns of 1.63% and −0.48%, respectively. The z-test results show that the models are statistically significantly different. We propose NSGA-II as the best candidate for use in pairs trading strategies in cryptocurrency markets.
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.
It is important to determine the network effects and store-of-value feature of cryptocurrencies due to the argument that it could be considered as a new ‘asset class’. Current studies on cryptocurrencies' network effects mainly focused on using Metcalfe's Law to evaluate the relationship between cryptocurrency prices and the squared number of active wallets addresses. In terms of cryptocurrencies' store-of-value features, previous studies primarily compared daily volatility of limited number of popular cryptocurrencies to Gold. Extant studies are also based on out-of-date data. This research extends the literature by using up-to-date daily data of a sample of the top 100 cryptocurrencies covering 2010–2023 to explore the network effects and the store of value characteristics of a wide range of cryptocurrencies. Firstly, we used nonlinear regression models to examine the relationship between cryptocurrency prices and active wallets addresses, the number of transactions and circulations. Secondly, to deepen our understanding of the store-of-value features of cryptocurrencies, we used a combination of GARCH models and time series analysis to explore the volatility in the daily returns of the sampled cryptocurrencies. Findings indicate that at least one of the network factors (i.e., active wallets addresses, the number of transactions, and number of circulation supply) have a significant effect on cryptocurrency prices. The study also finds that stable coins have comparable daily volatility as Gold, while only mature cryptocurrencies, such as PAXG, Bitcoin, Ethereum, BNB and LINK, demonstrate strong correlation with Gold. Bitcoin also showed a high positive time-series correlation with 24 of the 42 cryptocurrencies. Findings from this study provide important insights to investors, market analysts, regulators and other stakeholders on the marketisation and the store of value potentials of cryptocurrencies.
Over time, cryptocurrencies have experienced a widespread adoption, with bitcoin emerging as the most prominent example. In an increasingly uncertain world, the significance of possessing a stable store of value, traditionally fulfilled by gold, has escalated. Bitcoin has been often referred to as a digital equivalent of gold. Hence, this study primarily focuses on analyzing the price dynamics of this particular cryptocurrency. A comprehensive literature review will be employed to examine the regulatory obstacles encountered within the cryptocurrency market. Additionally, considering the contentious nature of this field, special attention will be devoted to the clash of perspectives surrounding this innovation. Subsequently, concentrating on the period 2016-2021, this paper will investigate the factors that define a risk-weighted investment, utilizing the Sharpe ratio and Sortino ratio. However, there has been significant volatility in the price of Bitcoin in 2020-2021, and our research fills a gap in the relationship between Bitcoin returns and risk in the post-2016 period. Overall, the analysis concludes that bitcoin exhibits highly turbulent investment characteristics. Despite its substantial price appreciation, the findings indicate that bitcoin displays significant volatility. Consequently, selecting this investment alternative entails considerable risks. Based on our results, there were years between 2016-2021 when bitcoin was a good investment, but in most cases its returns were associated with excessive volatility and risk. For this reason, it is not recommended for risk-averse rational investors.
Purpose This paper aims to analyze the connectedness between bitcoin (BTC) and other traditional assets (e.g. metals) in times of financial turbulence like the COVID pandemic. The purpose is to see to what extent BTC is mimicking the role precious metals are known for, that of being a reliable store of value. Design/methodology/approach The author relies on vector autoregressive modeling, as it yields a very flexible framework for forecasting and interpreting the interdependencies among variables, while providing a very intuitive framework when the underlying structural model is unknown. The author performs the analysis first for the whole sample and then for a “COVID-19 subsample.” Findings The author finds evidence supporting a stronger link between BTC and gold in COVID-19 times, with BTC the main driving force. The author quantifies the contribution of BTC to the surge in gold’s price during those early months of COVID-19 in the order of 28%, thus lending support to the idea of intrinsic or fundamental value in BTC. Practical implications Investors might consider including BTC in their portfolios as a long-term investment, very much like they do with gold. Social implications Cryptos at large represent an important check on the ever-expanding monetary policy pursued by central banks in recent times. Originality/value The COVID-19 pandemic has taken its toll on the world economy. It is thus timely to reevaluate the relationship between BTC and other assets such as gold and silver, which are traditionally seen as safe havens against uncertainty.
Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.
Ngô Thái Hưng, Toan Luu Duc Huynh, Muhammad Ali Nasir
Abstract This study investigates the impacts of economic policy uncertainty on the Bitcoin market using the monthly data from January 2014 to December 2022. In so doing, six major uncertainty indices (Global Economic Policy Uncertainty, Equity Market Volatility, Twitter‐based Economic Uncertainty, Geopolitical risk index, The Cryptocurrency Policy Uncertainty Index, The Cryptocurrency Price Uncertainty Index), and in particular, two novel Cryptocurrency Uncertainty indexes as introduced by Lucey et al. (2022) are taken into account. Our findings uncover a negative connectedness between Bitcoin prices and the key selected uncertainty indices, suggesting that higher uncertainties result in lower Bitcoin fluctuation across time and frequency domains. Our results provide valuable information on constructing asset portfolios for investors who have investment strategies entailing Bitcoin since Bitcoin would be a diversifier under economic policy uncertainty shocks. Our results hold robust by using the alternative methodology.
Abstract We examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.
This paper studies asymmetric spillovers from Bitcoin to green and traditional assets by using a full distributional framework established by a recently-developed Quantile-on-Quantile approach. The spillovers from gold to the same are further studied to compare the effectiveness of the underlying digital investment shelter of Bitcoin with its traditional counterpart of gold. Statistical evidence indicates that the cross-market spillover features evident asymmetry and non-linearity from three perspectives involving various quantiles of the joint distribution of dependent and independent variables, data in return and volatility, and before/after the COVID-19 pandemic. The investment sheltering role of Bitcoin is examined by its weakly positive, negligible, or even negative dependence with financial assets under different market conditions, while such the role is found to be relatively stronger for green assets compared to that for traditional assets. Moreover, the digital investment shelter is shown to be more effective than the traditional shelter given Bitcoin’s weaker or even more negative dependence with both green and traditional financial assets than gold. Additional analyses confirm the robustness of our findings that should be of interest to various stakeholders.
Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies.
Abstract In the present paper, the Granger causality test is used to study the causality relationships between Bitcoin and some of the most highly traded currencies, including euro, Japanese yen, British pound, Chinese yuan, and Indian rupee. To this purpose, the daily exchange rates of Bitcoin and the selected currencies to USD between 2014 and 2018 were used. Different from findings in existing literature, our study shows that there are no Granger causalities between Bitcoin and Euro, Japanese yen, British pound, and Indian rupee. A Granger causality is found in the direction from the Chinese yuan to Bitcoin.
Purpose: This paper analyzes the impact of the Russian invasion of Ukraine in February 2022 on returns of three groups of assets, i.e., commodities, stocks, and cryptocurrencies. Methodology: The study was conducted using the event study method which allows for quantifying the reaction of market participants to releases of various types of information. Findings: The cumulative abnormal returns (CARs) suggest a mostly positive effect of the conflict outbreak on returns of several commodities, especially precious metals. The obtained results suggest that in times of global crises, investors may consider precious metals as a safe haven. The study also indicates that on the event day the examined stock markets reacted negatively to information about the war, but to varying degrees. The Russian aggression against Ukraine did not affect the cryptocurrency markets in a statistically significant manner. Research limitations: The future studies related to the issue of the impact of Russian aggression against Ukraine on different markets may utilize larger research samples. They also may look for some factors affecting the reaction of markets to information related to the Russian military aggression, like the size of markets, trading volume, or geographical proximity, and economic dependence in the case of equity markets. Value: The study may provide some practical implications for both investors and regulators, especially in relation to the expected behavior of the markets and their informational efficiency in times of global crisis.
Open access
Environmental and Biological Research in Conflict Zones
This study investigates diversification potential in the Malaysian and United States (US) Islamic stock indices, Bitcoin, gold, and crude oil prices, particularly amidst economic crises. It uses wavelet coherence and MGARCH-DCC on a dataset spanning 2014 to 2022. The findings revealed that there are diversification potentials for investors. The dynamic conditional correlation (DCC) analysis indicated that the correlation of gold with both indices is generally low, except for a brief period of heightened correlation during the COVID-19 pandemic in 2020. The correlations between bitcoin and Islamic Stock Index Returns (ISIR) of the US and Malaysia respectively are generally weak across the study period except during the pandemic for the US. Hence, it is prudent for investors with exposure to the countries' stock index to incorporate gold within their portfolio to harness diversification benefits. The results further suggested that Bitcoin is also an appealing option for portfolio diversification. Our findings further revealed that during the Russia-Ukraine conflict, crude oil had demonstrated a minimal correlation with both the US and Malaysia ISIR, providing an opportunity for diversification. The results further suggested that Islamic equities can be a buffer against risk and instability, especially during turmoil, offering crucial implications for Shari'ah-compliant investors in Malaysia and the US. The study points to the need for further investigations incorporating additional economic shocks to understand diversification opportunities across varying investment horizons and holding durations.
Abstract The aim of this study is to compare and contrast the volatility of different asset classes namely Bitcoin, gold and crude oil against the US dollar, using the symmetric GARCH (1,1) model. Furthermore, this study examines which of the three assets provide the lowest volatility and identifies if the univariate GARCH (1,1) model can suitably forecast the volatility of the foreign exchange market, commodity market and cryptocurrency market. More specifically, this study uses only estimates from a symmetric GARCH model for the XAU/USD, WTI/USD and BTC/USD financial assets. Although the literature on the volatility of different assets is extensive, it neglects to detect the severe economic recession that is approaching. Considering that powerful nations purchased substantial quantities of gold in order to back their national currency, supremacy of the US dollar is under significant attack. Experts in international relations have claimed that it is essential to have a single, extremely influential national economy to exhibit stability and operate smoothly. Specifically, the international monetary system functioned under the theory of hegemonic stability. Given the fact that gold still remains the safe haven asset during periods of financial and economic distress, central banks are purchasing gold at a rapid pace with Russia and China leading the way. Furthermore, the demand for precious metals has also increased while the US dollar is struggling to keep its supremacy as BRICS reserve currency is seeking to replace it in the future. The daily data encompassing the necessary information for February 2012 - February 2020 is acquired from “Investing.com”, reaching 7193 observations. This study will enrich the literature associated with volatility forecasting of different asset classes during financial and economic turmoil while also raising awareness of the economic threats that could follow.
Abstract The fast development of the cryptocurrencies has brought to the attention of the authorities and researchers the importance of studying the risks associated with this category of assets. One of the main directions of analysis was the study of the correlations between the crypto-market and other traditional markets in order to assess the impact on financial stability. In the last 2-3 years, more and more studies have showed increasing correlations between the traditional markets and the crypto-market, which could generate some risks to the financial stability. We applied a novel methodology, based on TVP-VAR model, to study the correlations between Bitcoin and three other traditional assets, respectively gold, S&P 500 and EUR/USD from 01/01/2015 to 01/01/2023. We proved that the correlations between traditional assets, such as equity (S&P 500), respectively commodity (gold) and Bitcoin have increased significantly. However, other assets, such as the exchange rates are not correlated with cryptocurrencies and the correlations in the other way, from Bitcoin to gold, respectively S&P 500 are still very low. Thus, our findings indicate that, at this moment, the crypto-market poses risks to the financial stability, but because of the fact that the correlations are still only unidirectional (from traditional assets to cryptocurrency), the crypto-market could now just amplify the risks to the financial stability originating from the traditional markets.
Vera Mita Nia, Ossi Ferli, Irvan Novikri, Roy Sembel · 5 authors
Increasing market capitalization is characterized by high volatility but doesn’t have the ability and potential for monetary function, Crypto world eventually shifted into the most attractive investment in the digital economy. Numerous published studies have required some improvement in the consistent relationship between commodities and financial assets and the authors proposed an alternative assessment with demonstrating the relationship between the trading volume activity of the most traded cryptocurrency in Indonesia (i.e., Ethereum) and other investment assets in Indonesia such as market indexes, rupiah exchange rate against the dollar, and gold, and related to cryptocurrencies in Indonesia which observed in over the last three years. A Var model as a quantitative and statistical approach introduced and tested the stationary data with significancy value to identify the level of acceptance model. Consistency results from previous studies where Ethereum has the largest average return but higher risk and Gold as safer investment, ultimately diversification of the investment portfolio is suggested considering the degree of risk aversion. ABSTRAK Kapitalisasi pasar yang meningkat ditandai dengan volatilitas yang tinggi namun tidak memiliki kemampuan dan potensi fungsi moneter, dunia Crypto akhirnya bergeser menjadi investasi paling menarik di ekonomi digital. Sejumlah penelitian yang diterbitkan memerlukan beberapa perbaikan dalam hubungan yang konsisten antara komoditas dan aset keuangan dan penulis mengusulkan penilaian alternatif dengan menunjukkan hubungan antara aktivitas volume perdagangan mata uang kripto yang paling banyak diperdagangkan di Indonesia (yaitu, Ethereum) dan aset investasi lainnya di Indonesia seperti indeks pasar, nilai tukar rupiah terhadap dolar, dan emas, serta terkait cryptocurrency di Indonesia yang diamati selama tiga tahun terakhir. Model Var sebagai pendekatan kuantitatif dan statistik memperkenalkan dan menguji data stasioner dengan nilai signifikansi untuk mengidentifikasi tingkat penerimaan model. Hasil konsistensi dari studi sebelumnya di mana Ethereum memiliki pengembalian rata-rata terbesar tetapi risiko lebih tinggi dan Emas sebagai investasi yang lebih aman, pada akhirnya diversifikasi portofolio investasi disarankan dengan mempertimbangkan tingkat penghindaran risiko.
Sunčica Stanković, Bojan Đorđević, Nataša Milojević
The increase in the value of cryptocurrencies, market capitalization, and volume of trading on crypto exchanges resulted in a significant increase in the interest of researchers in this decentralized financial system. The two most popular cryptocurrencies today - bitcoin and ethereum - have captured the greatest attention of researchers. Given that cryptocurrency trading is similar to stock trading, the author's assumption is that their returns are determined by the price of gold and the volatility index – VIX, representing this paper's research hypothesis. Testing through vector autoregression (VAR) models, Granger causality tests, and impulse response function (IRF) shows that gold returns do not impact, unlike the VIX volatility index and Ethereum, indicating a significant relationship between cryptocurrencies bitcoin and US stock markets. On the other hand, Bitcoin returns and the volatility index cause ethereum returns, while gold returns do not.
Over the last decade or 1 - 1.5 years, financial markets have witnessed the mass emergence of virtual currencies based on blockchain technology, also known as cryptocurrencies. The most widely known is Bitcoin, the first representative, but today there are around 22,500 other cryptocurrencies. Many people see cryptocurrencies as an investment product with a high potential return, but are not aware of their operating mechanisms and the risks involved. There is a lively debate in financial circles on whether and to what extent crypto-currencies and their markets should be regulated. This paper will provide an overview of the characteristics of cryptocurrencies, their main types, possible directions for further development and the risks they entail, based mainly on international literature.