Fangying Liu, ChiâWei Su, Meng Qin, Muhammad Umar
Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system. First published online 30 August 2024
Hongjun Zeng, Qingcheng Huang, Mohammad Zoynul Abedin, Abdullahi D. Ahmed ¡ 5 authors
We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation ďźWQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies. ⢠After the outbreak of COVlD-19, the return interdependence became stronger. ⢠Copper functioned as a net sender of return spillovers throughout the entire observation period. ⢠The green bond market led the movements in the Lead and Aluminium markets at medium to long-term frequencies. ⢠Following the outbreak of COVlD-19, returns in the cryptocurrency market influenced the Copper and Lead markets. ⢠The cryptocurrency index consistently showed a positive correlation with the green energy-related metals market.
This article investigates the time-series properties of cryptocurrency returns and compares them with currency and commodity returns. We perform and analyze the mean reversion, normality, unit root, high and low returns, correlation, Autoregressive Moving Average (ARMA) [2,2], Autoregressive (AR) [5], and long-run components in the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [1,1] estimates. We also perform regression analyses to evaluate two possible behavioral biases: familiarity and disposition effect. Our time series analysis documents that cryptocurrencies are neither currencies nor commodities. We also show that adding cryptocurrency to a portfolio increases market efficiency and uncertainty. We also document that cryptocurrency investors exhibit the same familiarity and disposition effect biases as commodity and currency investors. Overall, we conclude that investors in cryptocurrencies tend to underestimate risk and misestimate future prices, as they do in commodity and currency markets. This study makes at least three contributions to the literature. First, we evaluate whether cryptocurrencies tend to hedge or financialization. Second, our analysis includes both univariate and portfolio dimensions. Third, this is a pioneering study on using behavioral bias analysis to determine whether a cryptocurrency is a commodity or a currency.
We analyzed Bitcoinâs cyclical patterns used by the Markov regime-switching model and explored the impacts of inflation and the US Dollar Index on Bitcoinâs cyclicality. The results showed Bitcoinâs cyclical pattern, the effects of the US dollar index and VIX on Bitcoinâs cyclical pattern, and how the US dollar index and VIX affect BTCâs structural changes in Bitcoin.
ÎĎνĎĎινĎÎŻÎ˝ÎżĎ ÎκίΝΝιĎ, Maria Tantoula, Manolis Tzagarakis
Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.
This study addresses a gap in the literature by exploring the impact of geopolitical risk on cryptocurrency markets, particularly Bitcoin, within different price and volatility regimes. We employed generalized autoregressive conditional heteroskedasticity (GARCH) and Markov-Switching Vector Autoregressive (MS-VAR) models on daily data from January 01, 2015 to January 15, 2024. We found evidence suggesting a strong positive relationship between lagged Bitcoin returns and current returns, indicating persistence or momentum in Bitcoin price movements. Additionally, heightened geopolitical risks were associated with decreased current Bitcoin volatility, particularly in state 1 characterized by lower price levels. Conversely, in state 2, which is characterized by higher price levels, geopolitical risk shocks initially spike, followed by a subsequent decrease in Bitcoin price volatility. Furthermore, shock analysis revealed nuanced reactions of Bitcoin prices and volatility to geopolitical events, with distinct patterns observed for different price regimes. Geopolitical risk can explain the variance in Bitcoin prices and volatility in lower-price-level states. These results suggest that adopting dynamic investment approaches that adjust to changing geopolitical conditions and market regimes can help investors navigate cryptocurrency market fluctuations more effectively.
This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.
Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis. Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poorâs 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model. Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period. Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.
Manali Agrawal, Rui Dias, Mohammad Irfan, Rosa Galvão ¡ 5 authors
Decentralised Finance (DeFi) provides a new way to perform complex financial transactions by exploiting blockchain's ability to maintain a decentralised ledger of transactions without being constrained by centralised systems or human intermediaries. DeFi provides alternative financial instruments that might lessen portfolio risk, especially given the erratic state of the financial markets today. This study analyses the association between the year of the coin in which it was introduced and the market capitalisation of the respective companies. Furthermore, the study also tries to understand the volatility associated with cryptocurrencies using EGARCH & GJR-GARCH models. The results reveal that market capitalisation is not similar for all three stages of the age of cryptocurrency. Also, negative news tends to impact Bitcoin more than positive news, and the volatility is persistent and long-lasting. Ethereum, BNB & Solana see more volatility from absolute past shocks; however, Tether exhibits low but persistent volatility as a stablecoin.
⢠Comparative analysis of Bitcoin and Ethereum electricity consumption and returns. ⢠Ethereum's transition to PoS shows a stronger link between returns and energy use. ⢠Shannon and RÊnyi transfer entropy reveal bidirectional information flow dynamics. ⢠Ethereum returns significantly impact energy consumption, unlike Bitcoin. ⢠A dynamic approach captures time-varying effects of market changes on energy use. Understanding energy consumption associated with cryptocurrency mining gained increasing attention, with the literature focusing mainly on Bitcoin. This study uses data from the two energy consumption indices, to estimate static and dynamic transfer entropies. The results provide a nuanced understanding of the bidirectional relationships and their implications. The dominant direction of information flow for Bitcoin is from electricity consumption to returns, while for Ethereum, it is from returns to electricity consumption, suggesting that Ethereum's returns significantly impact electricity consumption patterns. Results highlight the need for policies that integrate energy forecasting and environmental sustainability considerations and has significant implications for policymaking.
Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng
Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.
Quang Phung Duy, Oanh Nguyen Thi, Phuong Hao Le Thi, Hai Duong Pham Hoang ¡ 6 authors
Purpose The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023. Design/methodology/approach The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021â2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the LjungâBox test. Findings Using the BoxâJenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1). Originality/value The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.
David Umoru, Beauty Igbinovia, Isah Aisha Shaibu, Muhammed Adamu Obomeghie
The study examined the volatility of Bitcoin prices and volatility of exchange rates of oil-producing countries. The study used ARIMA, GARCH estimators for analysis. The study found ARCH effects in the data (heterskedasticity test; p<.05). The GARCH results laid credence to a confirmation of adjustments in the Bitcoin market having significant volatility influence on local currencies. Persistent volatility and volatility clustering found in some of the sampled countries denote increased risk and uncertainty in foreign exchange markets that stimulates increased borrowing costs and reduced liquidity. The actual and forecast values based on the ARIMA method match with an Out-of-Sample period plotted for forecast (27/12/2022 to 27/12/2024) except for Nigeria. The ARIMA models for UAE and Kuwait stand out with excellent fit and prediction accuracy. The poor ARIMA model for Nigeria was ascribed to the hyper-inflation in the economy and extremely volatile money market. In line with the efficient market hypothesis, significant interactions are pegged on available information being already reflected in the current value of the currencies. In effect, past currency rates and Bitcoin trading prices are useful predictors of future prices having factored in the relevant information that could influence currency's value. In addition, future values of local currencies can be forecasted from past values at a significant level of accuracy. Countries should ensure adequate regulation of the foreign exchange markets so as to curtail the wave of volatility risks on returns associated with Bitcoin trading and exchange rates.
Since the financial crisis, bitcoin has become a pioneer among virtual currencies, and much attention has been focused on its mechanisms, market risk and expected development. Despite extensive research into these aspects, the broader significance of bitcoin's existence has gone unnoticed. A critical facet is Bitcoin mining, notorious for its substantial energy consumption and subsequent carbon emissions. This dynamic interplay with the environment and energy market is a pivotal yet understudied aspect of Bitcoin's impact. Consequently, this paper seeks to fill this research gap by synthesizing existing literature on the repercussions of bitcoin mining on energy consumption and the environment. By delving into the intricate relationship between Bitcoin mining and its environmental consequences, the paper aims to shed light on a critical yet often neglected dimension. Furthermore, the analysis extends to examining the responsiveness of prevailing government policies to address the environmental concerns associated with Bitcoin mining. This endeavor underscores the necessity for a comprehensive understanding of the broader consequences of cryptocurrency activities, particularly in the realm of energy consumption and environmental sustainability.
Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify the most favorable parameter combinations and function prediction applications, ultimately obtaining the most accurate prediction results. The research process includes dataset selection, data processing, model construction, and training. Then adjust and improve the parameters used in the model, and record the process. Finally, test the model and output the test results. And model testing and result output. At the end of the experiment, the effects of different optimizers and parameters on the training results were compared, and the optimal combination was found. The model's predictive accuracy was evaluated through the examination of test data. This study can provide valuable references for researchers and firms.
Sonal Sahu, Alejandro Fonseca RamĂrez, JongâMin Kim
This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the marketâs continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.
Cryptocurrency has become a significant subject in the global financial market, attracting investors and traders with its high volatility and profit potential. This study analyzes the daily volatility and GARCH volatility of six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), USD Coin (USDC), Tether (USDT), and Ripple (XRP). Daily percentage change data and GARCH volatility are analyzed over specific time periods. The analysis reveals that Bitcoin (BTC) has an average daily percentage change of 0.366%, while Ethereum (ETH) has 0.376%. Litecoin (LTC) shows a daily percentage change of 0.166%, whereas USD Coin (USDC) and Tether (USDT) have very low daily percentage changes, nearly approaching zero. In terms of GARCH volatility, Ethereum (ETH) stands out with a volatility of 0.198, followed by Bitcoin (BTC) with a volatility of 0.121. The study's results indicate that cryptocurrencies are vulnerable to extreme price fluctuations, evidenced by their asymmetry distribution and kurtosis. Volatility correlation analysis reveals significant relationships, important for risk management and portfolio diversification. These findings contribute to understanding cryptocurrency volatility characteristics and aid stakeholders in making informed investment decisions.
Abstract This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural networkâlong short-term memory (CNNâLSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methodsâi.e., Boruta, genetic algorithm, and light gradient boosting machineâare applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNNâLSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
Identifying the expected growth in cryptocurrency involves analyzing a combination of market trends, technological advancements, regulatory developments, and economic indicators. Historical performance and adoption rates of major cryptocurrencies provide insight into market trends, while innovations in blockchain technology, such as Ethereum 2.0 and Layer 2 solutions, along with the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), highlight significant technological advancements. Regulatory developments, including supportive legislation and the involvement of institutional investors through financial products like Bitcoin ETFs, play a crucial role in shaping market confidence and investment. Economic indicators, such as inflation, monetary policies, and global events, also influence interest in cryptocurrencies as alternative assets. Investor sentiment, driven by public perception, media coverage, and social media activity, impacts market dynamics. Additionally, research from financial analysts, market research firms, and academic studies, along with corporate partnerships and the integration of crypto solutions with traditional systems, contribute to growth predictions. Monitoring market capitalization and trading volumes further helps gauge market interest and liquidity. By considering these multifaceted factors, a more comprehensive understanding of the potential growth in the cryptocurrency market can be achieved.
This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1,457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects - such as Sundays, certain months, and the COVID-19 period - are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations.
This study addresses a critical gap by providing an in-depth examination of how cryptocurrency markets respond to U.S. monetary policy shocks at various price levels. This study contributes significantly to our understanding of the nuanced dynamics governing cryptocurrency markets under diverse monetary policy conditions, thereby enhancing our knowledge of the broader financial ecosystem. Through rigorous quantitative analysis, we utilize monthly time series data spanning from January 2015 to December 2023 and employ models such as Markov-switching dynamic regression, Autoregressive Conditional Heteroskedasticity, and Generalized Autoregressive Conditional Heteroskedasticity. This study reveals that monetary policy shocks result in a decrease in cryptocurrency prices and volatility. Moreover, monetary policy tightening stabilizes the market at low cryptocurrency prices. In higher price states, interest rate increases are associated with reduced cryptocurrency prices and volatility. The findings suggest that changes in interest rates influence the opportunity cost of holding cryptocurrencies, impacting their appeal compared with traditional interest-bearing assets.
Bitcoin has increased rapidly in value since the first day of its integration into today's markets. The increases experienced have directed the interest of global investors to this field over time. In addition to these developments, the increasing popularity of blockchain technology and the increase in the volume of cryptocurrencies have turned these currencies into an important tool for commercial activities. Although there are many studies to measure the international trade balance with exchange rates, no study has been found to examine the relationship between the change in cryptocurrency prices and the trade balance of countries. In this study, the relationship between the trade balance of Nigeria, one of the leading countries in the world in terms of cryptocurrency usage, and cryptocurrencies is analysed using NARDL analysis with coefficient symmetry test (2016/M4-2020/ M12). According to the research results, Bitcoin and Litecoin can have significant long-term impact on Nigeria's trade balance.
One of the financial assets in currency exchange is now cryptocurrency. The public is drawn to cryptocurrency trading because it is considered a lucrative form of investing. For cryptocurrency investors to maximize their earnings, accurate price forecasting is crucial. As price forecasting involves time series analysis, a hybrid deep learning model is suggested to project cryptocurrency prices in the future. Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) networks are integrated into the hybrid model. Three cryptocurrency datasets are evaluated using the suggested hybrid model: Ethereum, Ripple, and Bitcoin. According to experimental results, the suggested LSTM-GRU model may provide the lowest MSE and RMSE values on the Bitcoin dataset (0.0611 and 0.2472), the Ethereum dataset (0.0369 and 0.19222), and the Ripple dataset (0.0006 and 0.0247).