Astitav Mittal, S. Hariharasitaraman, R. Raja Subramanian
Decentralized markets like Bitcoin and Ethereum, which utilize blockchain technology, offer advantages such as increased transparency, lower transaction costs, and quicker settlement times when compared to regulated markets. However, these markets are also known for their higher volatility. On the other hand, traditional regulated markets such as stock and commodity exchanges have also been subject to volatility due to macroeconomic factors such as inflation, geopolitical tensions, and policy changes. Therefore, it is crucial to compare and analyze the volatility of both decentralized and regulated markets to understand their behaviour and potential risks. This paper aims to conduct comparative market research and trend analysis of volatility in decentralized and regulated markets, investigating the contributing factors such as market size, liquidity, regulations, and the impact of market events like economic downturns or regulatory changes. The study will also examine historical trends, explore the correlation between the volatility of these markets, assess the potential impact of market volatility on investors and traders, and analyze how different trading strategies and investor behaviours can affect market volatility. The research could provide valuable insights into the behaviour of decentralized and regulated markets, which could be useful for investors, traders, policymakers, and other stakeholders. The paper will conclude with a discussion of the findings and their implications for traders and investors in both types of markets.
This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCCâARMAâGARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-Ă -vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 RussiaâUkraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.
This article provides a statistical approach to describe the fit of the most popular cryptocurrencies, building off a previous report, "A Statistical Analysis of Cryptocurrencies." We examined Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, and Doge coins. To model our cryptocurrencies, we utilized trading prices between 2017 and 2022 in light of historic events, such as the COVID-19 pandemic. Additionally, we performed a correlation analysis to help understand the relationship between the popular cryptos. Here, we report that the candidate distributions we fit to model the currencies needed to be more independent to describe the return of all popular cryptos. This could be due to the need for Correlation between some of these popular cryptos. We found the generalized hyperbolic and the generalized t showed the best performance of the models tested, though these approaches remained limited in their overall fitness. Their performance also varied by cryptocurrency under investigation, with Tether demonstrating the worst fit across all candidate models. Using our fit models, we also predicted the average daily returns for January 1st, 2023, to February 1st, 2023, and generally found good predictive validity. These results are critical in understanding the movements of cryptos and help better understand the risk associated with trading these currencies.
This study examines the day-of-the-week (DoW) and month-of-the-year (MoY) effects in the cryptocurrency market, with a focus on Bitcoin (BTC) and Ethereum (ETH). Due to the absence of a specific closing time in the cryptocurrency market, the closing time of the daily data is taken as 23:59 UTC. Initially, an appropriate volatility model for the cryptocurrency market is established using the GARCH, EGARCH, and TGARCH models. The most appropriate model for BTC is ARMA(1,0)-EGARCH(1,1) and ARMA(1,0)-GARCH(1,1) for ETH. The results of the analysis indicate a leverage effect in the cryptocurrency market, where negative shocks cause a more significant increase in volatility than positive shocks. Based on this volatility structure, the DoW and MoY are analyzed. For BTC, returns on other days are lower compared to Mondays. However, for ETH, returns on Thursdays are lower than those on Mondays. In terms of volatility, both BTC and ETH show that the highest volatility occurs on Mondays. For the MoY effect, neither BTC nor ETH donât exhibit a significant effect in the mean equation. Nevertheless, the variance equation indicates that January has higher volatility compared to other months, indicating the presence of a MoY effect in terms of volatility.
Tomas PeÄiulis, Nisar Ahmad, Angeliki N. Menegaki, Aqsa Bibi
Abstract This systematic literature review examines cryptocurrency forecasting trends, influential sources, and research themes. Following PRISMA guidelines, 168 articles from Q1 or Aâtier journals in the Scopus database were analyzed using bibliometric techniques. The findings reveal a significant increase in cryptocurrency forecasting research output since 2017, particularly in 2021. âFinance Research Lettersâ emerges as the most productive journal, whereas âEconomics Lettersâ receives the highest number of citations. Elie Bouri is identified as the most prolific author, and China is the top contributor country. Key research themes include bitcoin, cryptocurrency, volatility, forecasting, machine learning, investments, and blockchain. Future research directions involve utilizing internet searchâbased measures, timeâvarying mixture models, economic policy uncertainty, expert predictions, machine learning algorithms, and analyzing cryptocurrency risk. This review contributes unique insights into the field's growth, influential sources, and collaborative structures and offers a foundation for advancing methodology and enhancing cryptocurrency forecasting models.
Abstract This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the RussiaâUkraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.
The current research aims to reveal whether cryptocurrencies may be included in investorsâ portfolios as instruments for diversification and hedging against global systematic risk. The main contribution of the research is the fact that it provides proof of the usage of cryptos for hedging against global financial systematic risk. This seems to confirm the main hypothesis in the study about the role of money and cryptos in the contemporary global financial economy. The research reveals evidence that cryptocurrencies can play the role of global market predictors.
Abstract Cryptocurrency is a form of digital currency using cryptographic techniques in a decentralized system for secure peer-to-peer transactions. It is gaining much popularity over traditional methods of payment because it facilitates very fast, easy, and secure transactions. Social media is a significant influence, but it is also very volatile and subject to a variety of other factors. Thus, with over four billion active users on social media, we need to understand its influence on the crypto market and how it can lead to fluctuations in the values of these cryptocurrencies. In our work, we analyze the influence of activities on Twitter, in particular the sentiments of the tweets posted regarding cryptocurrencies and how they influence their prices. In addition, we also collect metadata related to tweets and users. We try to leverage these features to predict the price of cryptocurrency, for which we use some regression-based models and an LSTM-based model.
Abstract This study investigated the extent of currency competition within the cryptocurrency market through the Hayekâs concept of the denationalization of money. Hayekâs original analysis primarily centered on competition revolving around the medium of the exchange function. This study posited that cryptocurrencies compete across diverse monetary functions, particularly concerning their roles as speculative stores of value and exchange media. This assertion provided insight into the distinction between Hayekâs envisaged private currencies and the cryptocurrency paradigm. Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022, an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets. These findings reveal a robust competition among unpegged cryptocurrencies, predominantly contending for speculative investment purposes. Similarly, there is pronounced competition among stablecoins as stable stores of value. In contrast, competition is much less pronounced concerning the medium of the exchange function, potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.
Purpose: Bitcoin, the most popular form of virtual currency, currently holds the highest market capitalization among cryptocurrencies and serves as a benchmark for the typical cryptocurrency. The main goal of this research is to evaluate Bitcoinâs potential as a distinct asset class. This will be achieved by building upon previous studies and investigating its utility as both a hedging instrument and a tool for portfolio diversification. Methodology: In this study, Bitcoin is compared with other asset classes, such as key stock indices of Indiaâs Nifty-50 and Sensex, and key currency pairs with the Indian Rupee, including the US dollar ($), Euro (âŹ), Pound sterling (ÂŁ), and Japanese Yen („). Gold, as one of the most precise commodities, is analyzed using descriptive statistics to verify and confirm its properties as a distinct asset class. Additionally, the study employs the DCC-GARCH model to ascertain whether Bitcoin qualifies as both a hedging instrument and a tool for portfolio diversification. Findings: The findings of this study indicate that Bitcoins constitute a unique and separate category within alternative assets and investment classes. Various descriptive statistics confirm that Bitcoins exhibit characteristics of an asset class. Additionally, the study reveals and verifies the hedging and portfolio diversification capabilities of Bitcoin based on the results of the DCC-GARCH model. Practical Implications: The findings of this study will prove useful for investors considering cryptocurrency (Bitcoin) as an alternative asset class for diversifying their portfolios and hedging against volatility. Originality/Value: This study contributes to the research paradigm of Bitcoin finance by providing a perspective from a developing nation on Bitcoin as an asset class, which differs from other asset classes such as Nifty-50, Sensex, USDâINR, EURâINR, GBPâINR, JPYâINR, and gold. While previous research has predominantly focused on developed nation contexts, this study underscores the importance of examining Bitcoinâs role in portfolio diversification and hedging strategies. To enhance our understanding, this research presents daily observations of recent economic data spanning from 2011 to 2021. Assessing whether Bitcoin qualifies as an alternative investment and a distinct asset class is crucial, as it could significantly influence investment decisions and serve as a valuable tool for risk management and diversification purposes for investors.
Staking has emerged as a crucial concept following Ethereumâs transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs). We first quantify the price discrepancy between the LSD primary and secondary markets. Then we investigate and empirically measure how LTs can leverage such discrepancy to exploit arbitrage opportunities, unveiling the potential barriers to LSD arbitrages. In addition, we evaluate the financial profit and losses experienced by LPs who supply LSDs for liquidity provision. Our results show that 66% of LSD liquidity positions generate returns lower than those from simply holding the corresponding LSDs.
Bitcoin is an asset with high risks, and a significant part of its volatility can be explained by the speculative component. Parametric variance-covariance (VaR) methods are not applicable for assessing the risks of bitcoin investment, since log returns are not distributed according to the normal law. Autoregressive risk assessment models (such as ARIMA-GARCH) for bitcoin volatility overestimate risks at times of sharp exchange rate changes and they underestimate them at times of less significant rate changes compared to historical volatility. The grid search for the smoothing parameter in the exponentially weighted moving average method is potentially interesting for modeling the risks of bitcoin investment. This makes it possible to fully take into account the autocorrelation of the bitcoin rate to the levels of previous periods and the volatility of the asset. As a conclusion, there are currently no econometric models that can explain and forecast the volatility of bitcoin in the medium and short term, considering the available factors in the market.
Non-fungible tokens (NFTs) are unique digital assets that exist on a blockchain and have provided new revenue streams for creators. This research investigates NFT market inefficiencies to identify claimed cyclic behavior and cryptocurrency influences on NFT prices. The research found that while linear models are not useful in modeling NFT price series, models that extract periodic behavior can provide explanations and predictions of price behavior. The investigation of autocycles in cryptocurrency and NFT markets did not support the existence of Elliott Wave behavior in any of these blockchain enabled assets. Rather NFT price behavior is strongly tied to the underlying asset and its community of fans. These fans commit to periodic bouts of idiosyncratic trading which cools for a while, and then restarts. The research found no evidence supporting whole market effects across the full price series of individual NFTs. The research strongly supports prior findings that the offsetting movements significantly influence NFT prices and trading volume in Bitcoin and Ether. The research found NFT markets exhibit characteristics resembling a social media platform rather than more traditional asset markets like stock exchanges. It found that traditional linear econometric models cannot predict or explain NFT price series, only that NFT price and volume were weakly correlated. Fractal models consistent with Elliott wave theory do explain some of NFT price behavior, but are not consistent or stable over time. This research confirmed prior research findings that Bitcoin and Ether price movements are correlated with general NFT price and volume series in periods of between 24 and 48 h, with significant numbers of trades into and out of cryptocurrencies at 2 and 8 h.
This paper, âCryptocurrency Dynamics: An Analytical Exploration,â takes readers on a thorough exploration of the world of cryptocurrencies by combining in-depth analysis, data preprocessing, and the development of state-of-the-art models such as Gradient Recurrent Unit, Recurrent Neural Network, and Long Short-Term Memory. To ensure the accuracy and caliber of the Cryptocurrency dataset, this job begins with a thorough preparation of the data. In order to get the data ready for analytical study, this phase entails fixing issues including missing data, outliers, and the transformation of categorical variables. The next round of data analysis is where most of the work is done. Here, we use a variety of statistical and data visualization approaches to glean important insights from the cryptocurrency dataset. We closely examine relationships between different cryptocurrencies as well as market trends, trade volumes, and price volatility. By doing this, we find unseen trends, market dynamics, and important information that can inform investment choices and advance our understanding of this rapidly developing financial ecosystem. Additionally, by creating three distinct models and contrasting them, this work delves into the field of deep learning.
Eminda Ishan De Silva, Gayithri Niluka Kuruppu, Sandun Dassanayake
Purpose The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021â2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market. Design/methodology/approach This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods. Findings As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market. Originality/value This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.
The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.
Praise Otito Obanya, Modisane Seitshiro, Carel P. Olivier, Tanja Verster
Cryptocurrencies are widely regarded as volatile and less predictable assets by financial participants. The behaviour and dynamics of Bitcoinâs daily volatility, obtained by fitting GARCH models, are investigated for a period of 8 years using permutation entropy which is represented by the variable H for calculations. The best fitting GARCH models selected are the FIGARCH(1,0.7,1) and SGARCH(1,1) models based on maximum likelihood estimation, Akaike Information Criterion and Bayesian Information Criterion. Simulated volatilities are also obtained from the best fitting GARCH models using their respective parameters, to confirm how well the models fit. The results obtained show that the H values of Bitcoin are generally low and that the dynamics of Bitcoinâs volatility is quite predictable, as Bitcoinâs volatility is most likely to decline over time than increase or have an alternating movement. Also, the simulated volatilities show good agreement with the real-world volatility, confirming the models as good fits.
This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.
Digital currencies and coins are methods of computer-generated currency which uses cryptography for safety and operate individually of a dominant authority, like governments and economic institution. They are spread out and usually utilize blockchain technology to note transactions strongly. Cryptocurrencies such as Bitcoins, Ethereum, and some others have grown popularity in the latest eons for their latent to deliver borderless, secure, and fast transactions. Yet, they also arisen with threats like security concerns, regulatory uncertainty, and price volatility. It is vital for operators to conduct detailed research and comprehend the threats included before capitalizing or utilizing cryptocurrencies. Even though cryptocurrencies and coins have grown famous and are being gradually used for countless transactions, it is significant to remind that they still have not passed the traditional banking systems. Traditional banking sectors still perform an important part in the worldwide economic system, offering services like payment processing, savings accounts, and lending. Conversely, the growth of cryptocurrencies has directed to conferences about the possible influence on the financing sector and the necessity for traditional banking systems to acclimate to the varying setting of digital economics. It is important to observe these growths closely to comprehend the evolving association among the crypto-currencies and system of traditional banking. Hence, the current study gives the deep knowledge in the usage of crypto currencies, digital coins and their role in financial sectors that is dominating the traditional banking sector. And analyzed the impact of crypto currencies and digital coins on investors and economy of the nation and also regarding the easy accessibility of finance.