NgĂŽ ThĂĄi HÆ°ng, 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, Twitterâbased 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 longârange 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 preâ and postâepidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the timeâfrequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the postâepidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and longâterm fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of selfâsimilarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policyârelated 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 COVIDâ19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zeroâmean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate oneâdimensional lines, whereas stablecoins mimic twoâdimensional 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 Bitcoinâs 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.
Artor Nuhiu, Florin Aliu, Jakub HorĂĄk, Bedri Peci
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 âInvesting.comâ, 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 doesnât 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.
SunÄica StankoviÄ, Bojan ÄorÄeviÄ, NataĆĄa MilojeviÄ
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 â 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.
The purpose of this paper is to investigate whether the cryptocurrency market affects the financial stability and economic growth of India. The study used time series quarterly data on bitcoin, financial stability, inflation rate, real GDP, economic volatility uncertainty, exchange rate, and market volatility index for the period 2015Q1â2022Q4. The robustness of the findings was confirmed by the fully modified OLS (FMOLS) and canonical cointegration regression (CCR). The study results demonstrated that an increase in cryptocurrency investments will affect the financial stability of India significantly. Each 1% increase in the cryptocurrency would reduce the financial stability by 5% approximately. However, there was a marginal effect of cryptocurrency on economic growth. The results also found that exchange rate volatility and inflationary pressure would also deteriorate the financial stability of the country. Furthermore, the results also identified positive and significant cointegration between economic growth and financial stability. Due to most transactions in the economy being done through the financial system, it is paramount for economic growth. Going forward, aggressive monetary policy tightening, volatility in capital flows and exchange rates, de-anchoring of inflation expectations, faltering in the economic recovery, disruptions due to global supply chains and climate change will be the major risks to the financial stability and economic growth of India.
In the turbulent landscape of financial markets, Bitcoin has emerged as a significant focus for investors due to its highly volatile returns. However, the risks and uncertainties associated with it necessitate effective hedging strategies. This paper explores the potential of various financial assets, including interest rates, stock markets, commodities, and exchange rates, as dynamic hedges against Bitcoinâs risk. Utilizing a DCC-GARCH model, we construct a dynamic hedging model to analyze the viability of these financial assets as hedges. The data is categorized into pre-pandemic and pandemic periods to assess any change in hedging performance due to the outbreak of COVID-19. Our empirical findings suggest that the dynamic DCC-GARCH model outperforms the static OLS model in this context. During the pandemic period, a diverse set of financial assets demonstrated enhanced efficiency in hedging Bitcoin risk compared to the pre-pandemic phase. Among the hedging commodities, stock market indices, the US dollar index, and commodity futures displayed superior performance.
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.
This study aims to model the volatility features of Bitcoin, Ethereum, and Ripple, which are the cryptocurrencies with the greatest volumes that have come to the agenda since the global crisis, and to determine the presence and dates of price bubbles.After running the ADF and Ng-Perron unit root tests, the EGARCH model was analyzed as the best for Bitcoin and TGARCH for the Ethereum and Ripple. According to the obtained results, negative coefficients for Bitcoin imply that negative shocks will increase volatility more than positive shocks. This means that a leverage effect is present. No leverage effect was reached for Ethereum or Ripple, and positive shocks are understood to increase volatility for them compared to negative shocks. In addition, continuous speculative bubble pricing occurred for all three cryptocurrencies, with much higher bubble prices being understood to have occurred with Ethereum and Bitcoin compared to Ripple.
Shaista Arshad, Thi Hong Nhung Vu, Too Shaw Warn, Loke Mei Ying
This paper examines the hedging ability of gold, silver, and Bitcoin against inflation in ASEAN countries. The inclusion of Bitcoin as a hedge is relatively new in literature and the effect of hedging has been exacerbated post Global Financial Crisis, when the prices of precious metals have increased continuously. To serve that objective, the student-t EGARCH (1,1) model is first used to study the relationship between average asset return and inflation and next a quantile regression model is applied to explore the relationship between different quantiles of asset return and inflation. This ensures the hedging potential of each asset to be equally strong in bearish and bullish conditions. The tests show that the results from student-t EGARCH (1,1) model and quantile regression model are different while the results pre and post GFC are similar in most of the cases. The quantile regression model, which accounts for different quantiles for asset returns, indicates that gold, silver, and Bitcoin appear to be a hedge and safe haven in ASEAN countries. However, from the student-t EGARCH (1,1) model, which accounts for average asset returns, Bitcoin is a hedge asset but none of the three assets serves as a safe haven in ASEAN countries.
Abstract This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms across a set of 40 cryptocurrency prices. Results show that the evolving network topology of spillovers in both time and frequency domains helps towards a more comprehensive understanding of the interactions among cryptocurrencies, and that overall spillovers in the cryptocurrency market have significantly increased in the aftermath of COVID-19. Our findings indicate that a significant portion of these spillovers dissipate in the short-run (1â5 days), highlighting the need to consider the frequency persistence of shocks in the network for effective risk management at different target horizons.
Summary This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features NâBEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Samuel Asumadu Sarkodie, Mohammad Amin Amani, Maruf Yakubu Ahmed, Phebe Asantewaa Owusu
Bitcoin is a breakthrough financial technology but a volatile asset in financial markets with a complex fundamental consensus algorithm (Proof-of-Work) limiting its large-scale adoption due to environmental-related issues. Hitherto, the role of its technical and infrastructural composition that drives carbon footprint from an ecological perspective is rarely discussed in the literature. Here, we use machine learning and econometric techniques to analyze the past, present, and future changes in Bitcoin's carbon footprint with daily data spanning July 18, 2010 to December 04, 2021. We document technical drivers, decomposition effects, causal nexus, and implications of the Bitcoin blockchain's increasing energy and carbon footprint. We show that Bitcoin's technical drivers could have potential impacts on Bitcoin's carbon footprint, and subsequently, global climate change. For example, the network's hashrate increases mining difficultyââthereby increasing Bitcoin's energy consumption and subsequently, carbon footprint. We observed a direct association between the marginal effect of block size and transaction countââimplying that a higher block size improves transaction efficiency and then reduces Bitcoin's energy and carbon footprint. Besides, low mining difficulty increases market capitalization whereas increasing mining difficulty reduces bitcoin mining profit in the long run. This infers the reward for mining Bitcoin has a diminishing return in the long term. Thus, the adoption of advanced hardware for Bitcoin mining will spur energy and carbon intensity, yet will have a low return on investment. We highlight environmental regulations and regulatory changes that could limit Bitcoin's carbon footprint.
In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin's architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning $1$ year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.
While the majority of earlier studies used autocorrelation-based methodologies to explore the dependency structure for Bitcoin, this paper follows Benoit Mandelbrot in taking a fractal point of view. It shows that both Bitcoin and S&P 500 returns exhibit fractal-like behavior. Further evidence suggests that the infinite-variance-hypothesis cannot be rejected for both assets supporting Mandelbrotâs (1963) early study on cotton price changes. This result holds across non-overlapping subsamples. Following Mandelbrot (2008), Hurst exponents are estimated using rescaled/range analysis. The key findings are that (i) Bitcoin returns exhibit a higher level of persistence than S&P 500 returns across various subsamples, (ii) the level of persistence in Bitcoin returns has not changed across time, (iii) the S&P 500 moved from efficiency in the first subsample to inefficiency in the ex-post June 17, 2018 period, (iv) even if it was assumed that the variance of S&P 500 returns is finite, the kurtosis remains statistically undefined. The study concludes that correlation-based methods used to explore the S&P 500 universe result in misleading answers.