Volatility Of Cryptocurrencies Research Articles

Assessing the impact of the COVID-19 pandemic and the Russian–Ukrainian war on cryptocurrency volatility

Purpose This paper aims to investigate the impact of the COVID-19 pandemic and the Russian−Ukrainian war on the volatility of several cryptocurrencies. Design/methodology/approach To do this, the study uses the GJR-GARCH and dynamic conditional correlation (DCC)-GJR-GARCH models, which allow the author to estimate the conditional variance of the cryptocurrencies’ returns and assess their dependence structure over time. Findings The results show that the health crisis had a negative impact on all cryptocurrencies studied, except for Bitcoin, which experienced a positive impact. Additionally, the study finds that the Russian-Ukrainian war had a mixed impact on the cryptocurrencies studied, with some experiencing positive impacts (BNB, Dogecoin, Ethereum and Tether) and others experiencing negative impacts (Bitcoin, BUSD, Coin and XRP). Moreover, the author analyzes the spillover effects among the cryptocurrencies and observe significant interdependence during the periods under study. Originality/value Finally, the study discusses the implications of the findings for investors, policymakers and regulators, highlighting the importance of considering external factors when making investment decisions or designing regulatory frameworks for the cryptocurrency market.

Riding the waves of investor sentiment: Cryptocurrency price and renewable energy volatility during the pandemic-war era

This study explores the interplay between cryptocurrency price volatility and renewable energy dynamics amid crises, particularly during the COVID-19 pandemic and the Russia-Ukraine conflict. Utilizing a Quantile Vector Autoregression (QVAR) model with daily data from April 1, 2015, to September 23, 2022, we assess the interconnectedness of cryptocurrency volatility, investor sentiment, and energy fluctuations. Our findings reveal a significant temporal variation in systemic connectedness influenced by recent global events. Overall, we observe approximately 30 % connectivity in the short run and 6 % in the long run. Notably, Bitcoin and the Fear and Greed Index shifted roles from net shock receivers to transmitters over various periods. Financial and macro uncertainties primarily acted as shock transmitters during 2017 to early 2022. Furthermore, investor sentiment transitioned from a shock transmitter before the pandemic to a shock receiver during it. The analysis underscores the substantial impact of extraordinary events e.g., the COVID-19 pandemic, the Russia-Ukraine conflict on market dynamics.

The impact of the Russia-Ukraine conflict on market volatility: stability of Islamic cryptocurrency

Purpose - This research investigates how the Russia-Ukraine conflict affects the volatility of cryptocurrencies, with a specific focus on the comparative stability of Islamic gold-backed cryptocurrencies versus conventional cryptocurrencies, such as Bitcoin.Method - Utilizing the GARCH model, this study examines the risk factors and volatility transmission among cryptocurrencies (Bitcoin and Tether-gold), traditional financial markets (gold and stock markets), and their interrelationships during the conflict period. The study employs the daily closing prices of Bitcoin, Tether-gold, gold, the SP 500, and the Dow Jones Islamic Market Index from February 7, 2020, to November 30, 2023.Result - Bitcoin experienced significant volatility during the conflict, while the Tether-gold remained more stable. Islamic gold-backed cryptocurrencies have proven to be more stable than conventional ones during geopolitical crises.Implication - The findings offer valuable insights for investors seeking safe-haven assets during periods of economic uncertainty. Gold-backed cryptocurrencies present a more stable investment option compared to conventional cryptocurrencies, especially for investors adhering to Shariah principles.Originality - This research highlights the stability of Islamic gold-backed cryptocurrencies during geopolitical events, contributing to the understanding of safe-haven assets and offering practical implications for portfolio diversification and risk management.

The Role Of Blockchain Technology In Enhancing Financial Inclusion

Blockchain technology has emerged as a transformative force with the potential to greatly improve financial inclusion, especially among underbanked and unbanked communities. Financial inclusion refers to the accessibility and availability of financial services for all individuals, yet billions remain excluded from formal financial systems. Traditional banking often overlooks these populations due to geographic barriers, high transaction fees, and strict regulatory frameworks. Blockchain provides a decentralized, secure, and transparent platform for financial transactions, allowing individuals to access services without relying on traditional banking infrastructure. By removing intermediaries, blockchain lowers transaction costs, accelerates service delivery, and enhances transparency. It enables peerto-peer transactions, facilitates cross-border remittances, and supports micro-lending through smart contracts, thereby broadening access to vital financial services. Furthermore, the immutable and auditable characteristics of blockchain foster trust in financial systems, particularly in regions plagued by weak institutions or corruption. However, despite its promise, blockchain faces substantial hurdles such as regulatory uncertainties, limitations in technological infrastructure, and cryptocurrency volatility that impede widespread adoption. Integrating blockchain with complementary technologies like mobile banking and the internet could further enhance its role in advancing financial inclusion. This paper investigates the diverse effects of blockchain technology on financial inclusion while critically analyzing both its potential advantages and the challenges that must be addressed to fully leverage its capabilities in promoting global financial access and equity.

CRYPTOCURRENCY ADOPTION IN INTERNATIONAL MARKETS: CHALLENGES AND OPPORTUNITIES

Cryptocurrency is next in the list of gigantic financial innovations in modern times, changing the way any person, institution, and the government interacts through the global economy. Starting with Bitcoin back in 2009, it refers to decentralized digital currency based on the blockchain technology that can help someone carry out transparent, peer-to-peer, and safe transactions without using banks. There are numerous reasons that have fueled interest in cryptocurrencies, including financial independence, high returns on investment, and increased privacy in transactions. As cryptocurrencies take the world by storm in most international markets, they present major challenges and unique opportunities in equal measure. The acceptance of cryptocurrencies varies widely from country to country, depending on the regulatory environment, technology infrastructure, and cultural orientation toward digital finance. Areas with supportive regulations and the most advanced technological ecosystems promote rapid adoption, while restrictions in policies and limited technology access have hindered adoption in other areas. Apart from that, the inherent volatility of cryptocurrencies gives rise to several concerns with regard to using them as reliable mediums of exchange or stores of value, which may discourage potential users and investors. Security, fraud, and how a cryptocurrency works raise issues with trust, thereby making acceptance complicated. Despite these challenges, cryptocurrencies hold the potential for revolutionizing financial systems through financial inclusion in underbanked regions and facilitating faster, cheaper cross-border transactions. This paper aims to explore what factors drive the adoption or do not of the cryptocurrency by international markets and their related business and consumer implications. Through multiple case studies and insights obtained from various regions, the work attempts to deliver a profound understanding of the challenges posed by cryptocurrency adoption and at the same time the possible opportunities, in order ultimately to show the transformative value that cryptocurrency has in shaping the rapidly changing global economy.

Intraday and daily dynamics of cryptocurrency

This paper examines and compares intraday and intraweek patterns in hourly and daily prices, returns, volumes and volatility of native cryptocurrencies, stablecoins and tokens traded on Bitstamp. We show that native cryptocurrencies and tokens share common intraday periodicity determined by the operating times of the NYSE, LSE and Hang Seng stock exchange markets. Periodic patterns are also documented in the returns on cryptocurrency market portfolio approximated by the PCA applied to intraday and intraweek cross-sectional correlation matrices of cryptocurrency returns. Stablecoins have distinct dynamics and their daily and hourly returns are uncorrelated with one another and with the returns on other cryptocurrencies. We introduce a functional CAPM to accommodate the periodic patterns and estimate it by regressing the functions of intraday and intraweek cryptocurrency returns on the market portfolio. We show that the return functions on Bitcoin, Ether, and Link satisfy affine relationships with the return functions of the market portfolio and their functional betas display periodic intraday and intraweek patterns.

PREDICTIVE ANALYSIS OF CRYPTOCURRENCY INCOME USING MACHINE LEARNING MODELS

This research explores the potential of various machine learning models to predict cryptocurrency income. With the increasing volatility and widespread use of cryptocurrencies, accurately forecasting income from these digital assets is crucial for investors and stakeholders. By examining a dataset of cryptocurrency transactions, this study addresses the challenge of outliers and assesses the correlations between different variables to ensure robust data analysis. We employ three distinct machine learning models—Linear Regression, Decision Tree Regressor, and Random Forest Regressor—to train and evaluate the dataset. Each model is analysed to calculate performance metrics, such as Mean Squared Error (MSE) and R2 Score, which provide insights into their predictive accuracy and reliability. The detailed analysis and discussion highlight the strengths and weaknesses of each approach, emphasizing the efficiency and accuracy of the models in forecasting cryptocurrency income. The Linear Regression model, known for its simplicity, is compared against more complex models like Decision Tree and Random Forest Regressors, which can capture nonlinear relationships and interactions between variables. The Random Forest Regressor, an ensemble learning method, is particularly noted for its superior performance in handling complex datasets and providing more accurate predictions. This study's findings offer valuable insights for developing more effective investment strategies and risk management practices in the rapidly evolving cryptocurrency market. Through this comprehensive evaluation, the research aims to contribute to the growing body of knowledge on financial forecasting using machine learning techniques, paving the way for future advancements in this field.

Heterogeneity, Jumps and Co-Movements in Transmission of Volatility Spillovers Among Cryptocurrencies

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.

A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling

During the last years, cryptocurrencies have been increasingly becoming a relevant subject of academic researchers and investors. This paper adopts a novel framework that combines a multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and Copula modeling in a two-stage approach to analyze the cryptocurrency volatility dynamics. By combining the aforementioned techniques, on top of showing that price movements in one cryptocurrency can significantly influence others, the use of copulas highlight how these effects can vary across different parts of distributions and thus for different types of events with respect to their extreme nature. The interconnectedness complexity should be taken into consideration when managing risk in portfolio and constructing relevant models.

The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets

We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.

Quantitative Analysis of the Relationship Between Cryptocurrency Market and U.S. Stock Market Performance

With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.

Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model

In recent years, cryptocurrencies have gained investor attention for their extreme volatility, but this has introduced financial risks that require accurate prediction models. Therefore, we propose the SHARV-MGJR model, which incorporates both ‘good-bad’ volatility, leverage effects, and current return information to enhance the accuracy of cryptocurrency market volatility predictions. Empirical results demonstrate that compared to GARCH-type models, the SHARV-MGJR model exhibits superior predictive accuracy in forecasting cryptocurrency market volatility. Furthermore, robustness tests confirm the superiority of the SHARV-MGJR model in predicting cryptocurrency market volatility.

Innovation in Compensation Payments

The growing economic influence of cryptocurrency and the development of the associated infrastructure has brought about an interest in the new areas of its application. Payment of compensation in cryptocurrency is one such evolving application of cryptocurrency. Using a qualitative research design, we attempt to identify the factors and ecosystem constituents that play an important role in the diffusion, adoption and use of cryptocurrency as a compensation medium. A hybrid methodology involving both inductive and deductive methods of qualitative inquiry was used to develop the themes. The study identified nine main factors that influenced a participant’s decision to accept compensation in cryptocurrency. They are: (1) the prospects of high return; (2) ease of transaction; (3) faster international transactions; (4) price volatility of cryptocurrencies; (5) threat of hacking; (6) taxation policies of the country; (7) lack of recognition as legal tender; (8) influence of peer groups; and (9) influence of celebrities. The inductive part of the study enabled the mapping of the major stakeholders involved in the decision, namely, the government, the employer, the peer groups of the employee, celebrities and the cryptocurrency-issuing company.

Can U.S. macroeconomic indicators forecast cryptocurrency volatility?

This research examines the ability of 28 U.S. macroeconomic variables to forecast the volatility of six cryptocurrencies. In- and out-of-sample analyses are performed to validate their forecasting ability. Our analysis shows that during the full-sample period, 15 variables display forecasting ability, while post-COVID-19 period, this number is 17. Among these variables, the most influential include the consumer confidence index, leading economic index, consumer price index, U.S. exports and U.S. imports. Importantly, the predictive ability of these variables appears to have strengthened during the post-COVID-19 period. The out-of-sample results confirm the effectiveness of those macroeconomic variables in the in-sample tests. Furthermore, the robustness test reveals that incorporating these U.S. macroeconomic variables can enhance the performance of the GARCH volatility model. In this study, combination methods are used to enhance forecasting stability and are proven to have good forecasting ability. Our research also indicates that integrating global macroeconomic variables can enhance forecasting ability while recognizing the valuable information provided by U.S. macroeconomic variables. Additionally, we find that variables such as the short-term government bond yield and the M1 money supply emerge as important predictors of cryptocurrency bubbles.

Cryptocurrency volatility and Egyptian stock market indexes: A note

This paper examines the effect of the riskiness of the top four cryptocurrencies on the riskiness of stock market indexes in Egypt, being recognized as a developing country. The analysis uses daily data on cryptocurrencies and the three stock market indexes covering January 2020 to January 2023. The risk is measured using the holding period Value at Risk (VaR). The GMM results show that (a) cryptocurrency volatility is negatively associated with the volatility of stock market indexes. That is, the higher the investors’ interest in trading cryptocurrencies, the lower the volatility of stock market indexes as investors trade stocks less frequently, (b) cryptocurrencies can provide hedge and diversification benefits, and (c) the relationship between volatilities of cryptocurrencies and stock market indexes varies across indexes, therefore, contingent.

THE IMPACT OF CRYPTOCURRENCY ADOPTION ON TRADITIONAL BANKING SYSTEMS: A THEORETICAL STUDY

This comprehensive analysis delves into the intricate impact of cryptocurrency adoption on traditional banking systems. As cryptocurrencies achieve widespread acceptance, they challenge established norms of centralized control in financial transactions, necessitating a reconsideration of the resilience and adaptability of traditional banking models. The disruptive potential, particularly embodied in blockchain technology, prompts a critical evaluation of the ongoing transformation. The article scrutinizes the positive influence of cryptocurrencies on financial inclusion and accessibility, unlocking banking services for previously underserved populations. However, the decentralized and pseudonymous nature of cryptocurrencies introduces regulatory challenges, demanding a nuanced equilibrium between fostering innovation and ensuring compliance with anti-money laundering and know your customer regulations. Traditional banks respond by embracing blockchain technology, entering collaborative endeavors with cryptocurrency projects to augment operational efficiency and transparency. Nevertheless, the inherent volatility of cryptocurrencies poses systemic risks, necessitating adept navigation by traditional banking systems. The imperative for a delicate equilibrium between innovation and regulation emerges as pivotal for the harmonious coexistence of traditional banking and the ever-evolving cryptocurrency ecosystem. As the financial landscape undergoes profound changes, this analysis underscores the necessity for adaptability and a strategic alignment between traditional and innovative financial paradigms.

Beyond GARCH in cryptocurrency volatility modelling: superiority of range-based estimators

ABSTRACT Cryptoassets are extremely volatile with possible volatility jumps and infrastructure noise, making the estimation of true volatility process challenging. When the high-frequency data are not available, the true volatility needs to be estimated to be further studied or forecasted. The GARCH-family models have become a norm in the field. Here, we examine the performance of 6 GARCH-type specifications with 4 distributional assumptions and compare them with 4 non-parametric range-based models built on the daily ‘candles’. Our study focuses on five popular cryptocurrencies (Bitcoin, Ethereum, BNB, XRP, and Dogecoin) between 1 July 2019 and 30 September 2022, utilizing Binance 5-minute data for realized measures as the high-frequency estimators of the true volatility process. The results reveal that the Garman-Klass estimator clearly outperforms the GARCH-family models in all studied settings, and the other range-based estimators remain competitive with the GARCH-family models. These results are crucial for studies on volatility in cryptoassets where using the GARCH-type models is a standard. When the high-frequency data are not available, the range-based estimators, and the Garman-Klass estimator in particular, should be preferred as proxies for the true volatility process over the GARCH-type models, be it in the in-sample, more qualitative studies, or the forecasting, out-of-sample exercises.

The impact of the day of the week on the financial market: an empirical investigation on cryptocurrencies

PurposeThis study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week.Design/methodology/approachThe EGARCH model consists of the day of the week for 2019–2022 and the volatility of 11 cryptocurrencies.FindingsEmpirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends.Research limitations/implicationsThis study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments.Practical implicationsThe empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market.Originality/valueThe calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.

A comparative analysis of Silverkite and inter-dependent deep learning models for bitcoin price prediction

These days, there is a lot of demand for cryptocurrencies, and investors are essentially investing in them. The fact that there are already over 6,000 cryptocurrencies in use worldwide because of this, investors with regular incomes put money into promising cryptocurrencies that have low market values. Accurate pricing forecasting is necessary to build profitable trading strategies because of the unique characteristics and volatility of cryptocurrencies. For consistent forecasting accuracy in an unknown price range, a variation point detection technique is employed. Due to its bidirectional nature, a Bi-LSTM appropriate for recording long-term dependencies in data that is sequential. Accurate forecasting in the cryptocurrency space depends on identifying these connections, since values are subject to change over time due to a variety of causes. In this work, we employ four deep learning-based models that are LSTM, FB-Prophet, LSTM-GRU and Bidirectional-LSTM(Bi-LSTM) and these four models are compared with Silverkite. Silverkite is the main algorithm of the Python library Graykite by LinkedIn. Using historical bitcoin data from 2012 to 2021, we utilized to analyse the models’ mean absolute error (MAE) and root mean square error (RMSE). The Bi-LSTM model performs better than others, with a mean absolute error (MAE) of 0.633 and a root mean square error (RMSE) of 0.815. The conclusion has significant ramifications for bitcoin investors and industry experts.

Spillover effects according to classification of cryptocurrency

We use tail-event-driven network risk to analyze tail spillover effects and interconnectedness for three categories of cryptocurrencies: currency, protocol, and dAPP. Results indicate that the most prominent cryptocurrencies for incoming links and outgoing links are protocol (e.g., ETH and NXT). Currency and protocol cryptocurrencies are both receivers and emitters of systemic risk, whereas dAPP cryptocurrencies are only receivers. We also identify asymmetric effects in the incoming and outgoing links of cryptocurrencies at varying price levels. Some cryptocurrencies with smaller market capitalization have an outsized impact. Findings are important to understand the risk volatility and price spillover effects of cryptocurrencies.