This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.
Umar Nawaz Kayani, Mirzat Ullah, Ahmet Faruk Aysan, Sidra Nazir ¡ 5 authors
This study delves into an exploration of quantile connectedness across the domains of digital and traditional financial assets with the renewable energy prices index. The daily frequency dataset, spanning from January 02, 2018, to December 04, 2023, encapsulates diverse economic crises. Our inquiry elucidates distinctive patterns by employing empirical analyses utilizing quantile connectedness and Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodologies. In this context, DeFi assets (Chain-link) emerge as the primary recipient of information shocks, while Bitcoin distinguishes itself as the preeminent transmitter of such shocks within the network. Notably, digital assets manifest heightened volatility in contrast to traditional and energy indices. Furthermore, our findings underscore that the gaming industry, specifically focusing on Non-Fungible Tokens (NFT), presents itself as the most fitting asset for portfolio inclusion. This assertion gains credence from its comparatively lower degree of connectedness with other underlying assets. These findings have significant implications for investors and portfolio managers, furnishing valuable insights into the dynamics of asset interdependencies. Consequently, this aids in cultivating a more discerning approach to investment decision-making. ⢠Bitcoin is a significant transmitter of shocks, whereas DeFi assets like Chain-link predominantly receive them, highlighting their central roles in financial networks. ⢠Digital assets exhibit higher volatility than traditional and energy assets. The gaming industry, notably through Non-Fungible Tokens (NFTs), offers potential for portfolio diversification due to their minimal connectedness with other asset classes. ⢠The study provides critical insights into the interconnectedness of various assets, crucial for investors and portfolio managers to refine investment strategies and enhance decision-making.
Roland Akuoko-Sarpong, Stephen Tawiah Gyasi, Hannah Affram
The creation of cryptocurrencies has signified many consequences for financial markets of the traditional kind and their effectiveness. This research seeks to explore the effects of cryptocurrencies on a number of the other traditional markets in aspects of price discovery, volatility, interdependence, and information transmission. Event study analysis of everyday price changes and using multivariate cointegration analysis to cryptocurrencies and the evidence is that the cryptocurrencies are inefficient as characterized by irrational behavior, bubbles, and erratically fluctuating volatilities. However, they affect a range of currency, commodity, and stock market indexes by showing return and volatility spillover effects suggesting information flowing from one market to another. Alnet, cryptocurrency markets seem inefficient on their own but over time enhance the efficiency of linked traditional markets through participation and connectivity of global financial systems. The study contributes valuable insights into the evolving nature of financial markets in the digital era through discussions on market structure, behavioral factors, and policy implications.
Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti
We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices.
Febian Billy Sutanto, Eric Carlo Wibisono, Cindy Patricia, Edwin Hendra
This study examines the relationships between the United States and China stock markets, bitcoin, oil, and gold, during the COVID-19 pandemic and the Russian-Ukraine war. The data is extracted from the Bloomberg terminal, and through vector autoregressive regression model and Granger causality tests, relationships and effects between one financial asset and another are investigated. The key findings indicate a Granger causality where the lagged 2-day price of The U.S. stock market can predict the China stock market movement. Besides that, the lagged 2-day price of gold can also predict the United States stock market, China stock market, and crude oil on that day. This study gives insights for investors to understand what variables must be analyzed as a concern before making an investment decision during COVID-19 and the Russia-Ukraine War on each variable investigated in this research. Knowing about the relationship of each asset with vector autoregressive regressions an investor can select more relevant variables to be analyzed before making an investment decision. This research also gives insight for investors to predict the price evidenced by Granger causality during COVID-19 and the Russia-Ukraine War.
Abstract This paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlationâgeneralized autoregressive conditional heteroskedasticity (DCCâGARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be shortâlived, indicated by a relatively low DCCâGARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCCâGARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through realâworld examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the shortâlived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.
Arfan Shahzad, Yasmin Anwar, Muhammad Arif Nadeem, Waqas Shair
The advancement in technologies has changed the picture of todayâs economy. Cryptocurrency is the most trending currency nowadays. The form of cryptocurrency that is most commonly used in trading is Bitcoin. Since 2016, continuous fluctuations have been observed in the price of Bitcoin. The objective of the current study is to classify the strong predictor of Bitcoinâs price fluctuations and the associations of all these variables with each other. The price of several variables is selected as independent variables, including oil, VIX index, and US dollars. The price values for all study variables are collected for one year daily. The study findings indicated that lag 2 in the VAR model is the optimum lag for the model using HQIC and SBIC criteria, so todayâs price depends on the previous two daysâ price of independent variables. The correlation results indicated that the previous two-day price of EURO predicts the BTCâs todayâs price. A negative association is found between VIX and BTC. It is indicated that a 1 percent increase in the price of the VIX index will lead to the 60 decreases in BTCâs today price. The study also showed that it is not the price of BTC that forecasts todayâs worth of BTC, but it is the prices of VIX, euro, and oil that can predict todayâs price of BTC.
In this study, we investigate how adding Bitcoin can influence the investment portfolios. For this purpose, we consider a portfolio including Bitcoin and the five major sector indices of the Tehran Stock Exchange (TSE). At first, the asset returns are predicted through an estimation model based on higher moments. In the second step, the properties of Bitcoin in the face of other assets in a portfolio are studied by the asymmetric dynamic conditional correlation (ADCC) model. Then, the optimal weights in the portfolios are estimated. Accordingly, we used four portfolio optimization models with different objective functions, including a hybrid function of the higher moments, predicted risk from the ADCC model, and maximizing Sharpe and Sortino ratios. The out-of-sample results showed the relative efficiency of the proposed model in predicting the asset returns in Tehran Stock Exchange. In addition, the results of the ADCC model showed that Bitcoin plays a risk-hedging role for the pharmaceutical and banking sectors in TSE. We also know Bitcoin as a safe haven for the banking, petrochemical, metals, automobile, and pharmaceutical sectors. The results of portfolio selection also prove the effectiveness of adding Bitcoin with a maximum weight of 10% in the investment portfolios.
ABSTRACT Bitcoin, being one of the most triumphant cryptocurrencies, is gaining increasing popularity online and is being used in a variety of transactions. Recently, research on Bitcoin price predictions is receiving more attention, and researchers have investigated the various stateâofâtheâart machine learning (ML) and deep learning (DL) models to predict Bitcoin price. However, despite these models providing promising predictions, they consistently exhibit uncertainty, which cannot be adequately quantified by classical ML models alone. Motivated by the enormous success of applying Bayesian approaches in several disciplines of ML and DL, this study aims to use Bayesian methods alongside Long ShortâTerm Memory (LSTM) to predict the closing Bitcoin price and consequently measure the uncertainty of the prediction model. Specifically, we adopted the Monte Carlo dropout (MCâDropout) method with the Bayesian LSTM model to quantify the epistemic uncertainty of the model's predictions and provided confidence intervals for the predicted outputs. Experimental results showed that the proposed model is efficient and outperforms other stateâofâtheâart models in terms of root mean square error (RMSE), mean absolute error (MAE) and R 2 . Thus, we believe that these models may assist the investors and traders in making critical decisions based on shortâterm predictions of Bitcoin price. This study illustrates the potential benefits of utilizing Bayesian DL approaches in time series analysis to improve data prediction accuracy and reliability.
DomĂciĂĄn MĂĄtĂŠ, Hassan Raza, Ishtiaq Ahmad, SĂĄndor J. KovĂĄcs
Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.
Ijaz Younis, Muhammad Abubakr Naeem, Waheed Ullah Shah, Xuan Tang
This study analyzes the inter-dependence of the oil, gold, Bitcoin (BTC), and Gulf Cooperation Council stock markets during the recent RussiaâUkraine and IsraelâPalestine conflicts. The study found that these markets were less inter-connected during oil battles and the RussiaâUkraine conflict but more inter-connected during the COVID-19 crisis. Findings indicated that Oman, Kuwait, gold, and Qatar are the most significant spillover receivers, whereas the United Arab Emirates (UAE), Kingdom of Saudi Arabia, and West Texas Intermediate are the primary risk spillover transmitters in the IsraelâPalestine conflict. Additionally, BTC and the UAE are significant transmitters, whereas Kuwait and Qatar are the highest-risk spillover receivers in the RussiaâUkraine war. Portfolio estimates revealed that gold, BTC, and/or oil are useful in various equity markets for portfolio diversification and hedging under different market conditions and time horizons. These data can guide managers in portfolio construction and risk diversification. ⢠We examine the connectedness between oil, gold, bitcoin, and the GCC equity markets. ⢠Gold is the net recipient in all frequencies and sub-sample periods. ⢠Connectedness becomes lower in the oil battles, while higher in the COVID-19. ⢠Oil (bitcoin) is the net recipient during the oil battle periods. ⢠We estimate optimal portfolio weights and hedge ratios for portfolio strategies.
TecnolĂłgico de Estudios Superiores de Valle de Bravo, Adalberto GonzĂĄlez-Flores
The development of a monetary system that includes advancing the understanding of the factors that affect the price of cryptoassets. Halving is a unique event in the Bitcoin ecosystem that halves the reward per mined block. Studying its impact on the price would allow a better understanding of the supply and demand dynamics that determine the market value of bitcoin and other cryptocurrencies.
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 investigates whether investorâs sentiment and attention information collected via YouTube can improve bitcoin return forecasts. Accordingly, we collected daily data over the period 2017â2023, covering calm and turbulent periods marked by different types and episodes of emotions. Unlike previous studies, we used YouTube videos to propose two sentiment proxies: investor attention to YouTube (daily number of YouTube video views) and investor sentiment on YouTube (number of positive and negative videos on YouTube). Interestingly, we break down both attention and sentiment per subject. Econometrically, we assess lead-lag effects between sentiment/attention and bitcoin return using causality tests and Vector Auto-regressive (VAR) model. We also evaluate the forecasting power of YouTube attention/sentiment data using a deep learning LSTM model. Our study shows two main results. First, we find lead-lag effects between bitcoin returns and per subject investorâs attention and sentiment proxies. Second, we show that our deep learning LSTM model relying on the information provided by attention and sentiment supplants benchmark Buy and Hold Strategy to forecast future bitcoin returns.