John Kingsley Woode, Peterson Owusu, Anokye M. Adam, Emmanuel Assifuah-Nunoo · 5 authors
The study extends the literature on the nexus between cryptocurrency and uncertainty. This study proxied the cryptocurrencies and global uncertainty, respectively, with the seven most significant and variationally susceptible cryptos and the comprehensive world uncertainty in measuring the crypto-uncertainty nexus over the period (2015–2022) and further employing the quantile regression approach. The OLS model results point to a blend of both significant and insignificant relationship between global uncertainty and cryptocurrencies. These relationships were further examined in quantiles and further accounted for the impact of investor sentiments (VIX) and volatility (OVX), and the results were largely corroborated with the results from the conventional OLS, except for the Bitcoin, Litecoin, and Ripple markets. It was also discovered that the nexus changes across quantiles. The results revealed a blend of strong and weak hedges and safe havens among the selected cryptos against global uncertainty during normal and extreme market conditions. In the face of global turmoil, it was revealed that the average crypto market could serve as a safe haven. Also, the cryptos with an insignificant nexus with global uncertainty were found to be significantly affected by investor sentiment. These findings were further confirmed by the quantile-on-quantile and causality-in-quantile estimations. Given the intense precariousness and lack of hedge and haven capacities within the majority of the cryptocurrencies, it is pertinent for investors to consider the market in general as a means of diversifying their portfolios and reserve the hedge and haven option to the few markets that possess such luxury.
Kun Duan, Yanqi Zhao, Andrew Urquhart, Yingying Huang
This paper analyses time-varying networks of clean and dirty cryptocurrencies with green and traditional assets through a dynamic connectedness approach established by the time-varying parameter vector autoregressive (TVP-VAR) model. The underlying asymmetry of the dynamic pairwise connectedness when facing uncertainty shocks is further studied through a non-parametric quantile causality method. Our results demonstrate a limited information transmission of volatility from cryptocurrencies to both traditional and green assets, while the connection of clean cryptocurrencies (CI) with the financial system is even weaker compared to that of dirty cryptocurrencies (DI), especially after the COVID-19 pandemic. In contrast, connection within the financial system is found to be relatively closer. Moreover, causal relationships between economic policy uncertainty (EPU) and cryptocurrency-financial asset linkages are generally enhanced after the pandemic onset, while such the causality of uncertainty with DI related asset linkages tends to be even stronger. Most of the above causalities are shown to be negligible during market depression, further implying the sheltering role of the market linkages against uncertainty.
Quoc Minh Nguyen, Dat Tran, Juho Kanniainen, Alexandros Iosifidis · 5 authors
Many cryptocurrency brokers nowadays offer a va-riety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations.
Bitcoin has attracted incessant attentions in recent times. Studies have completed models to examine the relationship between Bitcoin and other multiple attendant variables. This paper considers a simple and direct price-volume relation. The paper offers causality evidence according to the dynamic asymmetric causality test. Based on available monthly data spanning 2010:M7-2022:M10, the paper shows that Bitcoin price and volume are integrated, both been I(0)’s. Moreover, the paper discloses the short- and long-term price-volume behaviors of Bitcoin using the cointegration test and vector error correction model (VECM). Taken together, the study first confirms long run relations and presents the estimates of the parsimonious VECM. The results show short run evidence of positive price-volume relations, and in the long run, the disequilibria are as well corrective and mean reversing. The outcomes of the Hatemi-J’s causality testing suggest likely evidence of bidirectional causality between the positive and negative fragments of the shocks of Bitcoin price and volume during the periods.
Wael Hemrit, Noureddine Benlagha, Racha Ben Arous, Mounira Ben Arab
Summary In this paper, we examine the connectedness between volatilities for various non‐fungible tokens (NFTs) and developed stock markets during the period from July 1, 2018, to June 15, 2022. With the use of the time‐varying connectedness methods to explore the volatility interdependences among these assets, we find that there is a significant volatility connectedness during Russia's invasion of Ukraine and COVID‐19 periods. Evidence emerging from this study advocates the inclusion of NFTs in developed stock markets for medium and long time periods only. The results also suggest that UK and Germany stock markets are the predominant market of spillover transmission, whereas the XTZ is the top net recipient/transmitter of volatility connectedness shocks. Moreover, Chinese stock market and ENJ offer more diversification gains than others, and the volatility connectedness from US stock market to NFTs is more pronounced in the long‐term than the short‐term. Our research provides some urgent and prominent insights to help investors and policymakers to be aware that NFTs are important hedge assets that should be added to stock portfolios during periods of geopolitical stability and in the post‐pandemic times.
The growing global fascination with cryptocurrencies has sparked heightened interest, driven by their pronounced market volatility. This particular study endeavors to assess the risk and rewards associated with four prominent cryptocurrencies, while also delving into an examination of their interrelationships and fluctuation patterns. The investigation is based on daily closing prices spanning from January 1, 2017, to June 30, 2022. To unravel the spillover and asymmetrical repercussions of volatility, we employ various models from the GARCH family, most notably the DCC GARCH and EGARCH models. In addition, Granger causality is harnessed to uncover any causal connections among these digital assets. The findings underscore a noteworthy spillover phenomenon between Bitcoin and Ethereum, the two foremost cryptocurrencies boasting the maximum market capitalization. This spillover effect manifests as symmetric volatility impacts, setting them apart from Litecoin and RIPPLE.
Abstract This study examines the asymmetric behaviour of Bitcoin relative to six major African fiat currencies (Egyptian Pound, Cedi, ZAR, Naira, Rupee and Dinar) for the period 10 August 2015 to 31 December 2022. The time and frequency information in the time series of the currencies were captured applying the ensemble empirical mode decomposition. The quantile regression (QR) and quantile‐in‐quantile regression (QQR) were applied on the decomposed series to examine the connections among the currencies at different currency regimes across time. The empirical results show that both QR and QQR can adequately capture the time‐varying asymmetric behaviour of the currencies across time. The results range from weak to very strong dependencies albeit both negative and positive across different quantiles. Our findings suggest that except for ZAR, Bitcoin is a viable alternative currency to African reserve currencies from the medium‐term since it can hedge depreciation and forex risk of the fiat currencies. Based on the findings of this study, we recommend that forex traders and policymakers in Africa should adopt Bitcoin as an alternative currency to African currencies in the medium‐term to mitigate currency crises in the continent.
Mohamed Fakhfekh, Yasmine Snene Manzli, Azza Béjaoui, Ahmed Jeribi
This article attempts to assess the hedging, diversification and safe haven characteristics of gold, Bitcoin and Tether for G7 investors during the political and health crises. For this end, we use the Generalized Autoregressive Conditional Heteroskedasticity-A-Dynamic Conditional Correlation model. The findings prove that gold can be considered as a strong safe haven asset for the G7 investors during the Russia–Ukraine crisis. In contrast, cryptocurrencies fail to retain their safe haven features for Japanese investors during the COVID-19 pandemic. But, they act as diversifier assets for the rest of the G7 stock markets. The computed optimal hedge and hedging effectiveness reveal that Bitcoin displays the best hedging instrument for the United States, British, Japanese and Canadian investors during the Russia–Ukraine crisis whereas gold is considered as the best instrument for German, French and Italian investors.
This article explores the extent to which network activity can explain changes in Ethereum transaction fees. Such fees are referred to as “gas prices” within the Ethereum blockchain, and are important inputs not only for executing transactions, but also for the deployment of smart contracts within the network. Using a bootstrapped quantile regression model, it can be shown that network activity, such as the sizes of blocks or the number of transactions and contracts, can have a heterogeneous relationship with gas prices across periods of low and high gas price changes. Of all the network activity variables examined herein, the number of intraday transactions within Ethereum’s blockchain is most consistent in explaining gas fees across the full distribution of gas fee changes. From a statistical perspective, the bootstrapped quantile regression approach demonstrates that linear modeling techniques may yield but a partial view of the rich dynamics found in the full range of gas price changes’ conditional distribution. This is an important finding given that Ethereum’s blockchain has undergone fundamental economic and technological regime changes, such as the recent implementation of the Ethereum Improvement Proposal (EIP) 1559, which aims to provide an algorithmic updating rule to estimate Ethereum’s “base fee”.
Kripto para piyasasının bilhassa son dönemlerde artan popülaritesi, piyasaların etkinliği ve geleceğe dair fiyat hareketlerinin anlaşılmasına yönelik ilgiyi de tetiklemişti. Bu çalışma, en yüksek işlem hacmine sahip 7 kripto para biriminin (Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Tether (USDT) ve Rippel (XRP)) piyasa üzerindeki etkinliğini Fama'nın (1970) etkin piyasalar hipotezi çerçevesinde incelemekte ve bu kripto paraların birim kök ve durağanlık yapılarına odaklanarak, piyasa üzerindeki etkinlik düzeyini anlamak ve gelecekteki fiyat hareketlerine dair bulgular elde etmektir. Bu sayede kripto para piyasalarında etkinliği daha iyi anlamak ve yatırımcılar için daha güvenilir yatırım stratejileri oluşturmak için sağlam bir temel sunmak mümkün hale gelebilmektedir. Araştırmada incelenen 7 kripto paranın fiyat davranışlarını analiz etmek amacıyla birbirine göre farklı avantajları ve bulunan farklı panel birim kök testlerinden faydalanılarak güvenilir ve sağlam sonuçlara ulaşma ihtimali arttırılmıştır. Analitik tekniklerden elde edilen bulgular araştırmanın anakütlesini oluşturan 7 kripto paranın rassal yürüş sürecine tabi olmamak (durağan bir sürece karşılık gelmek) suretiyle, zayıf formda etkin olmadığını göstermektedir. Kripto para piyasalarındaki etkinliğin anlaşılması, yatırımcıların daha bilinçli kararlar almasına yardımcı olacak ve finansal riskleri daha etkin bir şekilde yönetmelerini sağlayacaktır.
Stylianos Asimakopoulos, Marco Lorusso, Francesco Ravazzolo
We develop and estimate a DSGE model to evaluate the economic repercussions of cryptocurrency. In our model, cryptocurrency offers an alternative currency option to government currency, with endogenous supply and demand. We uncover a substitution effect between the real balances of government currency and cryptocurrency in response to technology, preferences and monetary policy shocks. We find that an increase in cryptocurrency productivity induces a rise in the relative price of government currency with respect to cryptocurrency. Since cryptocurrency and government currency are highly substitutable, the demand for the former increases whereas it drops for the latter. Our historical decomposition analysis shows that fluctuations in the cryptocurrency price are mainly driven by shocks in cryptocurrency demand, whereas changes in the real balances for government currency are mainly attributed to government currency and cryptocurrency demand shocks.
This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.
This paper investigates the dynamic relationship between cryptocurrency uncertainty indices and the movements in returns and volatility across spectrum of financial assets, comprising cryptocurrencies, precious metals, green bonds, and soft commodities. It employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach; the analysis covers both the entire sample period spanning August 2015 to 31 December 2021 and the distinct phase of COVID-19 pandemic. The findings of the study reveal the interconnectedness of returns within these asset classes during the COVID-19 pandemic. In this context, cryptocurrency uncertainty indices emerge as influential transmitters of shocks to other financial asset categories and it significantly escalates throughout the crisis period. Additionally, the outcomes of the study imply that during times of heightened uncertainty, exemplified by events such as the COVID-19 pandemic, the feasibility of portfolio diversification for investors might be constrained. Consequently, the amplified linkages between financial assets through both forward and backward connections could potentially compromise financial stability. This research sheds light on the impact of cryptocurrency uncertainty on the broader financial market, particularly during periods of crisis. The findings have implications for investors and policymakers, emphasizing the need for a comprehensive understanding of the interconnectedness of financial assets and the potential risks associated with increased interdependence. By recognizing these dynamics, stakeholders can make informed decisions to enhance financial stability and manage portfolio risk effectively.
Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.
In the long run, Bitcoin transaction fees are the only source of revenue for miners. They compete broadly in two main ways: proof of work effort to win blocks; and transaction processing to gather fee rewards into the blocks they win. This paper contributes to existing literature by developing a dynamic model that separates these two functions, and explores implications for aggregate efficiency outcomes. Specifically, when set by free market forces (unrestricted by artificially imposed block size caps), what happens to overall transaction prices and quantities relative to total energy use? When is it worth Stackelberg-leading miners investing in efficiency-improving R&D? What effect does this have on overall efficiency over time? By explicitly separating specialised capital dedicated to SHA256 hashing (for proof of work) from transaction processing capital (for transaction collection and verification), this paper sheds light on these questions. One key conclusion is that miner innovation lowers energy use per transaction over time for elastic enough transaction demand schedules. The more competitors Bitcoin has (existing fiat and data services, and other new Blockchain-based systems), the stronger is this conclusion.
<p>The objective of the study is to use daily Thai data analysis to strengthen correlations between Bitcoin and conventional asset measurements. The most popular asset prices and indices include gold, oil, the SET50 index, Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Dashcoin (DASH), Stellar Lumens (XLM), Binance coin (BNB), and Dogecoin (DOGE). We find a significant correlation between cryptocurrencies and the digital economy using a matrix approach to the Pearson correlation coefficient. With the help of a minimal spanning tree model and random matrix theory, we can determine the shortest route between assets. Yet, as predicted, only a small percentage of the greatest eigenvalues diverge. We are also developing a novel technique to find the SET-50 index. In an investment portfolio during the coronavirus period, alternatives to the gold price and the DOGE may offer possibilities for risk diversification.</p>
Purpose The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time. Design/methodology/approach The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer. Findings The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme. Originality/value The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.
Michael Demmler, Universidad Autónoma de Querétaro-Facultad de Contaduría y Administración
This study explores the financial performance of cryptocurrencies during the COVID-19 pandemic.In particular, the research objective is to compare the market price movements of the leading cryptocurrencies Bitcoin, Ethereum, BNB and XRP before and during the COVID-19 pandemic based on a longitudinal, exploratory, and quantitative research design which is centered on the analysis of the statistical moments of logarithmic return distributions, tests for structural changes combined with stationarity tests and portfolio optimization strategies.Results of the analysis show a clear change of the medium-to long-term return behavior of the analyzed cryptocurrencies during the pandemic, although not immediately after the pandemic announcement of the WHO in March 2020.Especially Bitcoin, BNB and Ethereum show comparable and even more favorable return characteristics in most samples compared to traditional investment alternatives.Furthermore, the diversification potential of cryptocurrency portfolios appears to be quite limited.
Luis Miguel Jiménez Gómez, Erick Lambis-Alandete, Juan D. Velásquez-Henao
Debido al alto atractivo de las criptomonedas, los inversionistas y los investigadores han prestado mayor atención en la previsión de los precios de las criptomonedas. Con el desarrollo metodológico del Deep Learning, la previsión de las criptomonedas ha tenido mayor importancia en los últimos años. En este artículo, se evalúan cuatro modelos de Deep Learning: RNN, LSTM, GRU y CNN-LSTM con el objetivo de evaluar el desempeño en el pronóstico del precio de cierre diario de las dos criptomonedas más importantes: Bitcoin y Ethereum. Se utilizaron métricas de análisis de desempeño como MAE, RMSE, MSE y MAPE y como métrica de ajuste, el R2. Cada modelo de Deep Learning fue optimizado a partir de un conjunto de hiperparámetros y para diferentes ventanas de tiempo. Los resultados experimentales mostraron que el algoritmo RNN tuve un rendimiento superior en la predicción del precio de Bitcoin y el algoritmo LSTM en el precio de Ethereum. Incluso, ambos métodos presentaron mejor desempeño con dos modelos de la literatura evaluados. Finalmente, la confiabilidad del pronóstico de cada modelo se evaluó analizando la autocorrelación de los errores y se encontró que los dos modelos más eficientes tienen alto poder de generalización.