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.
The ecological structure of the cryptocurrency market and its external environmental impact cannot be ignored. Taking Bitcoin as an example, the study analyzes the Bitcoin market during the COVID-19 pandemic crisis from an environmental perspective based on the differences in the political and economic systems of China and the United States. First, the institutional environment selected in this study is used as an indicator of environmental measurement and divided into formal and informal sectors. In the analysis, the former selects the economic policy uncertainty index of China, while the latter selects bitcoin’s social attention (including Google trends and the Baidu index) and investor sentiment as indicators. Second, financial market data such as foreign exchange and commodity futures are used as indicators for analyzing the environment of the bitcoin market. Using VAR modeling analysis, the results show that both the institutional and the market environment have an impact on bitcoin’s market performance.
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.
As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision.
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.
We investigate the interconnectedness between major cryptocurrencies and foreign exchange rates. This study employs time-series daily data for the cryptocurrencies and foreign exchange rates closing prices, the data is obtained from investing.com and yahoo finance to cover the period of 10 November 2017 to 18 January 2022. The study adopts the connectedness approach developed by Diebold Yilmaz (2014), using the TVP-VAR model to analyze twelve cryptocurrencies and eight foreign exchange rates. The results reveal a greater degree of connectedness across cryptocurrencies and foreign exchange rates over the whole sample, pre and during the corona pandemic, indicating that the Corona pandemic donates to the increase of volatility spillover across the currency and cryptocurrency markets. The results further show that Ethereum, Bitcoin Cash, Litecoin, Bitcoin, TRON, Cardano and Ripple are the main transmitters of shocks to other cryptocurrencies. Moreover, the EUR/USD, AUD/USD and NZD/USD are the main transmitters of shocks to other foreign exchange rates. The study has significant implications for investors, and portfolio managers. Our results offer evidence to improve financial risk assessment, and portfolio hedging strategies of cryptocurrencies against the uncertainty raised by Covid-19 Pandemic, which our findings may support investors in properly rebalancing their portfolios as the level of uncertainty in the market changes.
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.
Adhi Dharma Wibawa, M Sadewa Wicaksana, Yuri Pamungkas
Cryptocurrency has become one of the most widely used digital investment instruments worldwide. In simple terms, cryptocurrency is a digital currency. Cryptocurrencies are not available in physical forms, such as coins or cash, but are commonly used worldwide. In cryptocurrency, everything is completely virtual. Even so, this digital money has a fairly high value. In 2017, its market capitalization reached 18 billion USD. In addition, cryptocurrencies carry a very high risk due to the problem of very fast and irregular price changes. Therefore, a study on the link between cryptocurrency and other commodities is required. Thus, data with a strong correlation may be used to judge cryptocurrency transactions. As a result, in this study, we employ the Pearson Correlation to determine the association of cryptocurrencies with various datasets, including gold, oil and natural gas, and the Nasdaq stock market. Consequently, the Nasdaq stock market dataset outperforms the others in terms of correlation, with the greatest value on the Google index of 0.91 and 0.92 on bitcoin and Ethereum. Regarding oil and natural gas, Ethereum has the greatest value versus crude oil (at 0.74), while bitcoin is at 0.61.
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.
Forecasting data and research on cryptocurrency price forecasting methods are increasing in importance. So far, methods based on LSTM deep learning architecture have shown the best results in forecasting cryptocurrency prices. In order to improve the accuracy of forecasting data, this paper investigates the application of a multivariate multistep forecasting method based on the LSTM deep learning model for the bitcoin price time series and evaluates its effectiveness. The variants of multivariate multistep forecasting implementation based on deep learning LSTM are analyzed, and a direct approach for building multistep forecasts is chosen. Time series of bitcoin price and cumulative stability and drawdowns are used as input data. Based on our research, we found that short-term predictions were most accurate using models trained on trading data. However, for long-term forecasts, incorporating stability features slightly improved accuracy.
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.
Purpose This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war. Design/methodology/approach Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model. Findings The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis. Originality/value This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.