Muneer Shaik, Mustafa Raza Rabbani, Youssef Tarek Nasef, Umar Nawaz Kayani · 5 authors
In this study, we investigate the dynamic volatility connectedness of fintech, innovative technology communication, and cryptocurrency indices for the period from June 2018 to June 2022. We investigate the connectivity and risk spillovers before and after the COVID-19 period to understand the volatility fluctuations by employing the dynamic connectedness measures based on TVP-VAR methodology. We find that volatility connectedness is strong among the Fintech, and cryptocurrency indices and it increase further during uncertainty caused due to COVID-19 pandemic & also during escalations of Russian-Ukraine war period compared to the pre-pandemic levels. We identify the net transmitters and net recipients of volatility among the fintech, innovative technology communication, and cryptocurrency indices. We observe that spillovers among the variables under study are dynamic in nature and shift from net recipients to net transmitters of volatility and viceversa during different time periods Our study has beneficial implications for policymakers, regulators, investors, and financial market constituents to redevelop their existing strategies and understand the fourth industrial revolution and new economies' indices to avoid financial losses during the financial markets’ turmoil.
Non-fungible tokens are a revolutionary concept that combines art, authenticity, proof of ownership and enables large-scale commerce. Their value does not come from their use in financial transactions, but from the fact that they are linked to specific assets, whether digital or real. Non-fungible tokens thus represent a new driving force in the areas of digital ownership. Owners of these tokens can earn huge sums at a time when the art market is on the verge of revolution. The non-fungible token market experienced enormous growth in 2021 with traders investing a huge amounts of billions of dollars worth of cryptocurrencies into digital collectibles. Since 2021 the transaction activity in this area cooled, although the number of active investors continued to grow in 2022. While the popularity of certain non-fungible token collectibles can fluctuate depending on market conditions, some traders may try to manipulate the prices of certain non-fungible tokens to make them appear more valuable. The token will be sold at a higher price to a new wallet, which is also controlled by the original owner. Transactions between wallet addresses are saved on a blockchain and can be accessed publicly, so that anyone can see when the token was traded and for how much it was sold. However, wallet addresses contain no identifying information making it very difficult to discern who is behind a transaction and whether two addresses are owned by the same individual. This process is called wash trading and its analysis is a goal of this contribution.
Abna Ajeesh, Lekshmi Prakash, Mohammad Ali Moni, V. Sreeraj
This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.
Bitcoin has received a lot of attention as a cryptocurrency in recent years. The paper focuses on determining if Bitcoin would replace world currency in the future, which compares Bitcoin with the US dollar and gold. The article considers qualitative analysis to discuss the bitcoin's characteristics based on Karl Marx's five different categories in his book Das Kapital: measure value, means of circulation, means of hoarding, means of payment, and universal currency. The paper presents the advantages and disadvantages related to Bitcoin compared to the US dollar and gold. The article finds that Bitcoin is more secure in saving and privacy. Also, it wouldn't be affected by inflation. However, disadvantages are also present when applying Bitcoin in the market. The volatility and value of storing would be a challenge to solve. The lack of population using it and the limitations of transactions would also be problems. The research illustrates that Bitcoin can't replace world currency in the short term, but in the long term, Bitcoin would be the mainstream currency to use in real life due to the performance of people, governments, and the world economy. The article is the first research to utilize Marx's theory to analyze whether Bitcoin could replace world currency in the existing literature. This paper recommends that governments of all countries establish a unified regulatory policy and security mechanism on a global scale to ensure the legitimacy, stability, and security of Bitcoin so that Bitcoin can truly become a world currency.
Sumanjay Dutta, Parthajit Kayal, G. Balasubramnaian
This article investigates the dynamic relationship between cryptocurrencies and metals, examining the existence and direction of volatility spillovers. While previous studies have explored the relationships between different cryptocurrencies and between base metals and gold, there is a notable gap in understanding the volatility spillover nexus among cryptocurrencies. This study makes a significant contribution by employing the Time-Varying-Parameter-Vector-Autoregressive (TVP-VAR) total connectedness measure to assess the strength of association between these assets. Our analysis employs 10-year daily returns data for three cryptocurrencies (Bitcoin, Litecoin, and Ethereum) and two metals (Gold and Copper). As we witness major economic events worldwide, this study is particularly relevant, as it provides insights into potential hedging opportunities. To comprehend the risk contagion patterns, various measures of partial and dynamic connectedness are computed, supporting the earlier TVP-VAR analysis. The findings indicate that Litecoin and Ethereum exhibit a high level of connectedness, while Bitcoin remains relatively less connected. Among the metals, Gold and Copper demonstrate similar levels of connectedness in certain cases. Notably, there is a significant risk contagion between Litecoin and metals. These results hold essential implications for policy-makers and portfolio managers with different time horizons, offering valuable insights into risk contagion within the cryptocurrency and metal markets. JEL Codes: C32; G15; G17; G41
Methodologies to infer financial networks from the price series of speculative assets vary, however, they generally involve bivariate or multivariate predictive modelling to reveal causal and correlational structures within the time series data. The required model complexity intimately relates to the underlying market efficiency, where one expects a highly developed and efficient market to display very few simple relationships in price data. This has spurred research into the applications of complex nonlinear models for developed markets. However, it remains unclear if simple models can provide meaningful and insightful descriptions of the dependency and interconnectedness of the rapidly developed cryptocurrency market. Here we show that multivariate linear models can create informative cryptocurrency networks that reflect economic intuition, and demonstrate the importance of high-influence nodes. The resulting network confirms that node degree, a measure of influence, is significantly correlated to the market capitalisation of each coin ($ρ=0.193$). However, there remains a proportion of nodes whose influence extends beyond what their market capitalisation would imply. We demonstrate that simple linear model structure reveals an inherent complexity associated with the interconnected nature of the data, supporting the use of multivariate modelling to prevent surrogate effects and achieve accurate causal representation. In a reductive experiment we show that most of the network structure is contained within a small portion of the network, consistent with the Pareto principle, whereby a fraction of the inputs generates a large proportion of the effects. Our results demonstrate that simple multivariate models provide nontrivial information about cryptocurrency market dynamics, and that these dynamics largely depend upon a few key high-influence coins.
In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data. In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data.
Timothy Kayode Samson, Christian Elendu Onwukwe, Adedoyin Isola Lawal
With escalating public interest in the cryptocurrency market, largely driven by its perceived potential for rapid wealth accumulation and various advantages over traditional currencies, there is an imperative to understand its inherent volatility.This study addresses the dynamic behaviour of cryptocurrencies by utilizing skewed error innovation distributions to model the volatility of five key cryptocurrencies.Data was sourced from Yahoo Finance, encompassing daily closing prices from September 11, 2017, to April 8, 2022.The significance of the skewness parameter in all optimal volatility models (p<.05) substantiates the application of skewed error innovation distributions.Notably, the observed influence of past negative events on volatility was consistently greater than that of positive events across most examined cryptocurrencies.While Value at Risk (VaR) models are frequently used for risk measurement in this domain, this study's findings suggest that their reliability is not universal across all cryptocurrency cases.Consequently, caution is advised when employing VaR models for risk assessment associated with cryptocurrencies.
This study provides a comprehensive analysis of the evolving field of Bitcoin research through the application of bibliometric analysis and structural topic modeling techniques. A dataset of 1,937 articles from the Scopus database, spanning the period between 2013 and 2023, was examined, with a specific focus on the prominent digital currency, Bitcoin. The analysis encompasses publication trends, influential journals, authors, institutions, and impactful articles in the field. Through the application of structural topic modeling, six distinct thematic clusters in Bitcoin research are identified, encompassing topics such as blockchain-based digital currency, volatility modeling, portfolio diversification, Bitcoin futures trading, return forecasting, and cryptocurrency regulations. Furthermore, this study outlines future research directions in the domains of finance, economics, and management pertaining to Bitcoin. By bridging the gap in existing literature and providing valuable insights, this study aims to support the development of effective risk management strategies, regulatory frameworks, and business approaches. The findings serve as a valuable resource for industry practitioners, academics, regulators, and policymakers navigating the evolving landscape of cryptocurrencies.
Muhammad Mahmudul Karim, Md Hakim Ali, Larisa Yarovaya, Md Hamid Uddin · 5 authors
Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.
The aim of this work is to utilize the kernel regression (KR) approach to predict the closed-price for cryptocurrencies. This study makes use of three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The min-max normalization method was used to scale feature values to a common range, often between 0 and 1. Furthermore, support vector regression (SVR) and long-short term memory (LSTM) were used to compare the prediction model-based on KR. The result of the KR models utilizing RMSE and MAPE demonstrated that the predictive model-based on KR gave more satisfying results.
The study investigates the relationship between the returns of Non-Fungible Tokens (NFT) and its categories; and fear indices during times of crisis. The fear indices considered are Global Fear Index (GFI), Global Economic Policy Uncertainty Index (GEPU), Twitter based Economic Uncertainty Index (TEU), Global Consumer Confidence Index (CCI), Infectious Diseases Equity Market Volatility Index (IDEMV) and Crypto Volatility Index (CVI). Employing Granger Causality Test, Autoregressive Distributed Lag technique and ARDL Bounds test on data for the period starting 1st February 2020 and ending 28th February 2022, it is found that short run association exists between TEU, CVI and NFT returns. Further, GFI leads NFT Art returns while TEU leads NFT Metaverse returns by lag 5 and lag 2 respectively. No association between fear metrics and NFT Collectible, NFT Game and NFT utility is observed. No long run association in found between NFT returns and fear indices except TEU which influences NFT returns. It is concluded that NFT, NFT Art and NFT Metaverse returns have positive association to at least one fear index during times of turmoil, especially for the short run.
While Bitcoin has been a hot topic in the investment world due to its rising value, gold remains a popular investment option. To create a value prediction model for the best investment strategy, we utilized LSTM and found that it had a higher fitting effect than other two models, grey prediction and time series. The accuracy rate is 88.7%, the loss rate is 0.135%. We test different batch sizes to ensure the accuracy of prediction and established an appropriate algorithm to calculate the best investment strategy for each day. To test the accuracy of the model, we use four methods, including testing the accuracy of the risk factor in the model, observing the growth of total asset value, calculating the error rate of the investment process, and performing robustness analysis under low investment costs. We also perform sensitivity analysis to determine the impact of transaction costs on the strategy and results. The results show that the fluctuation in Bitcoin transaction costs is more significant and can affect the frequency of trading activities, ultimately affecting the final profit.
Cryptocurrency is no longer an unfamiliar concept. With the development of the digital economy, cryptocurrencies have gradually replaced some functions of traditional currencies. This research aims to measure and evaluate the impact of cryptocurrencies on financial markets by considering their effects on exchange rates, gold prices, oil prices, and stock indices. Data for the analysis were collected on a weekly basis from 1 January 2014 to 28 February 2021. The multiple linear regression model was used to examine the relationships in the research model using the statistical analysis software SPSS 22. The research results indicate that cryptocurrencies have an impact on the financial market. Specifically, the research also identified the inverse effect of currency pairs on cryptocurrencies and the interaction between different cryptocurrencies. Consequently, financial market regulators, especially the agency responsible for monitoring the volatility of cryptocurrencies, exchange rates, gold prices, oil prices, and stock indices, have a basis for devising appropriate plans. From this research, managers can implement policies that enhance financial education and communication to help individuals understand the nature of virtual assets, especially cryptocurrencies, while creating motivation towards accepting cryptocurrencies in Vietnam.
Bu çalışmanın amacı, Bitcoin fiyatında meydana gelen değişimlerin bankaların finansal performansı üzerindeki etkisini belirlemektir. Bu doğrultuda BIST Banka endeksinde bulunan 10 bankanın, 2017-2022 yılları arasındaki çeyrek dönem verileri araştırma dönemi olarak belirlenmiştir. Finansal performansın tespiti için verilerin incelemesinde ve araştırılan etkinin tespitinde Panel Veri Regresyon analizinden yararlanılmıştır. Regresyon analizini uygulamadan önce değişkenler arasındaki ilişkiyi belirlemek amacıyla korelasyon analizi yapılmış ve değişkenler arasında negatif bir ilişkinin varlığı tespit edilmiştir. Ardından Panel regresyon analizi için iki model oluşturulmuştur. Modellerin analizi sonucunda ise her iki modelde de bağımsız değişken olan Bitcoin fiyatı ile bağımlı değişkenler arasında anlamlı bir ilişki tespit edilemezken, kontrol değişkeni ile finansal performansı ifade eden bağımlı değişkenler arasında negatif ve anlamlı bir ilişki olduğu sonucuna ulaşılmıştır.
This paper explores the relationships between the US dollar, crude oil, gold, and bitcoin by taking into account the higher-moment linkages. Specifically, we construct robust estimators for the realized volatility, realized skewness, realized kurtosis, and jump, and study the causalities between the estimators through the Granger causality test. A generalized impulse response analysis identified by our quad-variate VAR specification is further implemented to uncover the lead-lag spillover effect across the variables of interest. We utilize high-frequency data for the chosen assets from January 3, 2016, to June 23, 2022, and observe various patterns of cross-market interconnection related to higher-order moments. These findings suggest that systematic risk factors must be considered while jointly modeling market linkages. Practical implications for investors and market regulators are also discussed.
John W. Goodell, John W. Goodell, Miklesh Prasad Yadav, Junhu Ruan · 7 authors
This paper analyses the connectedness among traditional assets, digital assets and renewable energy for extending the data from December 31, 2019 to January 2, 2023. For an empirical analysis, time varying parameter (TVP-VAR) is employed. We find that Chainlink (DeFi) is the highest receiver, while bitcoin is the highest transmitter of shocks to the network. Additionally, we also find that Non-Fungible Tokens (NFT) acts as the most suitable asset to be included in portfolio since it is least connected with rest of the examined assets classes. Results are important for investors and portfolio managers.
The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.