Volatility Of Cryptocurrencies Research Articles

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.

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.

Do design features explain the volatility of cryptocurrencies?

This paper examines the impact of cryptocurrency design features on their return volatility. We compile a sample of 58 cryptocurrencies, adopt the taxonomy of design features proposed by Eska et al. (2022), and estimate LASSO regressions. We document that older cryptocurrencies tend to be less volatile. Networks with mandatory transaction fees, cryptocurrencies based on (delegated) Proof-of-Stake, and those developed by private for-profit entities tend to be more volatile. Furthermore, we provide evidence that networks passing transaction fees and/or tips on to verifiers are associated with higher volatility levels.

Flight-to-safety across time and market conditions

This paper examines the time-varying directional predictability from the implied volatility of risky assets, such as US stocks, to the implied volatility of less risky or safe haven assets, such as gold, cryptocurrency, foreign exchange rates, and US Treasury notes, which allows for making time and regime dependent inferences on the flight-to-safety phenomenon. The results show a positive directional predictability from the VIX to the implied volatility of gold, cryptocurrency, and foreign exchange rates in periods of high market uncertainty such as the COVID-19 outbreak and the war in Ukraine. The volatility of US Treasury notes has a different response dynamic to the VIX, switching to negative cross-correlations in some cases, given its tight link to inflationary pressure and economic policy uncertainty. The main results remain qualitatively unchanged after incorporating economic policy uncertainty and oil volatility. An additional analysis indicates that flight-to-safety is not exclusively triggered by the high volatility regime in US equities but happens at median levels too. Our findings have important implications for investors, risk managers, and policymakers regarding volatility predictability, hedging, and market stability.

Volatility modeling of cryptocurrency and identifying common GARCH model

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student’s t, Generalized Error, Skew Normal, Skew Student’s t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations.

Trends and Triggers: Analyzing the Co-Movement of Cryptocurrency and NFT Prices

This paper aims to explore the complex interrelationships between the prices of cryptocurrency, specifically Ethereum (ETH), and five top Non-Fungible Token (NFT) collections: Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, Moonbirds, and Otherdeed. Motivated by the intertwining dynamics of these digital assets and the unexplored nature of their interdependencies, this study employs a Vector Autoregressive (VAR) model and utilizes Granger Causality to dissect the multifaceted interactions. The analysis period ranges from April 2021 to January 2023, a critical window of exponential growth and fluctuation in the digital asset market. The results demonstrate a statistically significant impact of ETH prices on NFT collection prices, but not vice versa, revealing the strong dependence of the NFT market on cryptocurrency volatility. Specifically, the research finds that changes in ETHs value are predictive of shifts in NFT prices, whereas NFT price fluctuations lack predictive power for ETH prices. In conclusion, this research represents an advancement in understanding price dynamics in the rapidly evolving digital economy. By innovatively analyzing the co-movement of cryptocurrencies and NFTs, it not only enriches existing knowledge but also paves the way for further exploration, offering practical insights for diverse stakeholders navigating this exciting, ever-changing field.

Estimation of realized volatility of cryptocurrencies using CEEMDAN-RF-LSTM

Predicting cryptocurrency volatility is crucial for investors, traders, and decision-makers but is complicated by the market’s high non-linearity, volatility, and noise. This paper presents a novel approach, the CEEMDAN-RF-LSTM hybrid model, which is the first to combine the strengths of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Random Forest (RF), and Long Short-Term Memory Network (LSTM) to predict the Realized Volatility (RV) of mainstream cryptocurrencies. The model exploits CEEMDAN’s proficiency in processing non-linear and non-stationary signals, RF’s exceptional feature selection capabilities, and LSTM’s distinctive advantages in dealing with time-series problems. Applied to actual transaction data for Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB), empirical results show the superior performance of our model in predicting actual cryptocurrency volatility. These findings contribute to the academic understanding of cryptocurrency volatility and provide practical guidance for quantitative trading strategy development, offering fresh insights and methodologies for related research fields.

The Downsides of Digital Currency and the Limits of its Normative Regulation

The paper reflects on the consequences of the bankruptcy of the FTX exchange in the context of cryptocurrency volatility, as for the legal anchoring of cryptocurrencies in terms of de lege lata and de lege ferenda, especially in the context of the need for agile proactive state response to criminal activities connected to the use of cryptocurrencies. The nature of cryptocurrencies predisposes the commission of criminal activities related to crypto predominantly in the online space, resonating with the necessity to increase transparency and control of cryptocurrency transactions. The anonymity of coins and the limited ability to track them gives them a clear technological advantage over the efforts of law enforcement bodies to map them or check them by regulatory authorities. The subject of the empirical part of the paper is the analysis of a case study of a regulatory offense committed by a selected service provider in the field of performing exchange activities in the Slovak Republic.

Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures

ABSTRACT In this paper, we investigate interdependencies between cryptocurrencies and investor sentiment by introducing the asymmetric TVP-VAR frequency connectedness approach. Our empirical results provide evidence of pronounced and time-varying interconnectedness between sentiment and cryptocurrency. In addition, we find that negative short-term interconnectedness dominates positive short-term interconnectedness until mid-2020 when this effect was reversed, persisting until the end of the sample period. While Ripple and Bitcoin are both found to be the main net transmitters of shocks, Stellar, Ethereum, and NEM are considered net receivers of shocks. Overall, our findings suggest that market sentiment is mainly driven by cryptocurrency volatility in both the short and the long run.

Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study

The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (FinTech) in managing these risks and assess the adequacy of current regulatory frameworks. Employing a quantitative research approach, the study collected data from 386 participants, including FinTech practitioners and investment portfolio managers through a survey that combining closed-ended and open-ended questions. Multiple regression was used to analyze the relationships between variables such as FinTech integration, regulatory compliance, and the risk associated with cryptocurrency-inclusive retirement portfolios. The study revealed that the inclusion of Bitcoin significantly increases portfolio volatility. It also found that advanced FinTech data management techniques significantly enhance risk mitigation, while current regulatory frameworks are inadequate for governing the inclusion of cryptocurrencies in retirement plans. A comprehensive framework combining FinTech and regulatory compliance was shown to effectively reduce investment risks. The study recommends cautious consideration of cryptocurrencies in retirement portfolios, with an emphasis on assessing the risk appetite of participants. It advocates for dynamic regulatory frameworks and enhanced use of FinTech for real-time risk management. The study suggests that retirement plan providers should adopt an integrated approach, combining technological innovations with regulatory oversight, to navigate the complexities of cryptocurrency investments effectively.

Decoding the cryptocurrency user: An analysis of demographics and sentiments

In recent years, new payment methods have emerged, aimed at improving convenience for users. Cryptocurrencies, in principle, are no different. In this study, we seek to analyze the general population's attitudes towards the adoption of cryptocurrencies as a payment method. To achieve this, we have developed a descriptive survey that targets both current cryptocurrency users and non-users, recognizing that differences in perception may exist. Additionally, we have conducted a sentiment analysis of open-ended questions to understand respondents' views on the future of the cryptocurrency market and its potential as a payment tool, utilizing different lexicons in the English language. Our findings indicate that most cryptocurrency users prefer to invest in these digital assets, often choosing coins based on their popularity rather than other intrinsic features. E-commerce payments are the most attractive activity, followed by international transactions when using cryptocurrencies as a payment method. However, high volatility and a lack of ease of use are the most common difficulties reported by users. Our study also highlights the importance of regulation in a time when users are increasingly demanding higher levels of oversight, in contrast to the past. While users are concerned about the instability and volatility of cryptocurrencies, they also value the anonymity these transactions offer. Our analysis showcases an innovative approach to analyzing interviews and qualitative questionnaires that can be applied in other research fields.

Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis

Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis

Internet publishing as a forecasting tool in the crypto market

Subject. This article examines the relationship between the sentiment caused by the news on the CoinTelegragh professional forum and the changes in Bitcoin, Litecoin and Ethereum cryptocurrencies. Objectives. The article aims to assess the impact of the sentiment of various Internet publications on the volatility of cryptocurrencies, as well as the predictive power of Google Trends and the VIX Index for cryptocurrencies. Methods. For the study, we used the cross-quantilogram method and the VADER sentiment analysis model. Results. The article finds that the Google Trends Index in a short period of one to three days can be used to predict the closing prices of Bitcoin, Litecoin, and Ethereum, while the VIX Index (Stock Market Uncertainty) has no relationship with the cryptocurrency market. This means that cryptocurrencies can be used as a safe-haven asset when the background market is highly volatile. Conclusions. The crypto market has a complex sentiment component, with its prices and trading activity determined by popularity, emotion, and sentiment. The findings confirm previous studies, which claim that during the period of prevalence of negative news and publications, the crypto market gets narrowed, the trading volume drops off, and the interest of Internet users gets low to a minimum. The euphoria in the market, on the contrary, attracts new unqualified investors, and this is confirmed by the number of views of basic information about cryptocurrencies on Wikipedia.

The implication of cryptocurrency volatility on five largest African financial system stability

This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.

Co-volatility dynamics in global cryptocurrency and conventional asset classes: a multivariate stochastic factor volatility approach

Purpose This study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds. Design/methodology/approach It used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation. Findings The study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics. Originality This study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

A Wavelet Analysis of Bitcoin Price Volatility Dynamic

The cryptocurrency market has experienced significant growth in international finance in recent years, attracting a large number of investors. This has led to a substantial increase in the overall trading volume[1], surpassing 1.2 trillion USD in July2023. Bitcoin in particular, has garnered significant interest from both advanced and emerging economies. Notably, Bitcoin is legal tender in El Salvador and the Central African Republic. The cryptocurrency market possesses distinct characteristics that set it apart from traditional markets such as exchanges, commodities, and equities. Its decentralization, facilitated by Blockchain technology, stands as a key differentiating factor. This technology enables anonymous trading of cryptocurrencies, with the identities of market participants, account holders, and electronic wallet managers remaining unknown. Consequently, there is an inherent ambiguity when it comes to characterizing the behavior of these investors or determining their preferred trading horizons. In this context, an important question arises: how do investors in the cryptocurrency market navigate price volatility? Furthermore, how do their trading strategies, which are inherently tied to their investment horizons, impact each other and consequently influence market prices? The heterogeneity hypothesis, which is a prevalent assumption in financial markets, is particularly relevant when exploring how investors in the cryptocurrency market navigate price volatility. The diverse trading strategies and approaches to handling price fluctuations among heterogeneous investors impact and influence the overall volatility and price dynamics of cryptocurrencies. The heterogeneity market hypothesis[2] refers to the existence of differences among investors or traders regarding their beliefs, information, risk preferences, investment strategies, and time horizons. In a heterogeneous market, investors, traders and financial institutions have varying views and expectations about the future performance of assets. These differences can manifest in various ways, such as market information access, market liquidity provision, trading strategies and market structure. While numerous empirical studies have explored the concept of market heterogeneity in relation to traditional assets such as those conducted by Müller et al. (1993;1997), Lux and Marchesi (2000), LeBaron (2000), Dacorogna et al. (2001) and Benhmad (2011), there is a paucity of research on this topic specifically focusing on cryptocurrencies. This paper aims to contribute to the existing literature that investigates the hypothesis of investor heterogeneity in traditional markets, including forex, stocks, and commodities. Specifically, we focus on the cryptocurrency market, which is predominantly dominated by Bitcoin. As of November 2021[3], Bitcoin holds the distinction of being the most actively traded cryptocurrency, with a market capitalization exceeding 1.2 trillion USD. Given its significance, Bitcoin garners substantial interest from investors across various categories. Therefore, this research seeks to uncover potential synergies among participants[4] in the cryptocurrency market and identify investor types and investment horizons prevalent in this market. By understanding these factors, we can gain insights into the dynamic volatility displayed by Bitcoin’s price. To tackle this issue, we investigate Bitcoin’s price volatility, analyzing causal relationships among short-term, medium and long-term traders by using wavelet transform to decompose volatility at different trading frequency scales[5] considered, then Granger causality test will be employed to determine if changes in one scale impact others. Furthermore, we extend this analysis by employing the nonlinear causality test proposed by Hmamouche. Y (2020). This nonlinear causality test goes beyond the limitations of the linear causality test and helps identifying potential nonlinear causal effects that may exist between the volatilities at different frequency scales, which could be overlooked by the linear causality test. The rest of this paper is organized as follows: Section 2 presents the theoretical framework relatively to the heterogeneity market hypothesis. Section 3 focuses on the empirical review. Section 4 and 5 turns to the data and methodology. Section 6 provides the empirical findings. Section 7 concludes.