Tonuchi E. Joseph, Atif Jahanger, Joshua Chukwuma Onwe, Daniel BalsalobreâLorente
Abstract 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 Moroccoâs 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.
Abstract Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.
This study examines whether precious metals, industrial metals, energy and agricultural commodities, or cryptocurrencies form trustworthy safe havens against extreme price volatility of major global bank stock indices during black-swan events such as the COVID-19 pandemic and the Russia-Ukraine conflict. Using daily data and applying Quantile-VAR dynamic pairwise and extended joint connectedness methodologies, we investigate dynamic connectedness between major financial assets and major bank indices during exceptional crises. Findings provide evidence that crude oil and both Ethereum and Bitcoin present evidence of propagating significant shocks towards bank stock indices during crises, but other large-cap cryptocurrencies present no evidence of any specific influence. Further, gold, natural gas, and wheat are identified as the main absorbers of spillovers from banking indices during analysed crises, with more pronounced effects identified during exceptional phases of volatility. Such findings suggest that risk in the banking sector can be efficiently hedged by traditional safe havens such as gold and counterbalanced by highly outperforming assets such as natural gas and wheat. The study significantly contributes to understanding the interplay between banking sectors and various financial assets during crises and the subsequent strategies available for managing systemic risks, providing valuable insights for policymakers, regulators, and investors alike.
Ahmed Bossman, Mariya Gubareva, Samuel Kwaku Agyei, Xuan Vinh Vo
The growth of digital assets in recent periods are accompanied by negative externalities which raise concerns over sustainability. This has influenced the news content on both conventional and social media outlets, leading to the creation of the index of cryptocurrency environmental attention (ICEA). Given the pivotal role of social and conventional media in forming investorsâ attitudes and behavior in financial markets, we address this issue from the perspective of Islamic stocks, which by their nature represent a class of Shariah-compliant sustainable assets. With the dataset spanning from 2014 onwards up to July 2022, we analyze how the ICEA induces the market dynamics in Islamic stocks covering diverse economic sectors. By applying the bi-wavelet-based time-frequency econometric framework, our empirical findings reveal time-varying levels of coherence between the ICEA and Islamic sectoral stocks, implying that the pricing and returns-generating dynamics across various economic sectors in Islamic markets are led by media coverage on environmental attention vis-Ă -vis the mining and trade of cryptocurrencies. Notwithstanding, our results indicate that the real âbrick-and-mortarâ categories of faith-based stocks, which contains the basic materials, consumer goods, industrials, and oil & gas sectors, provide attractive diversification attributes. Our findings are important for risk, portfolio, and policy management.
Innovative financial services may help to reduce global carbon emissions. We examine the activity in trading of voluntary carbon credits on a new blockchain-based exchange, which reduces the amount of intermediation in this market. Over the years 2021 and 2022, about 3.8 million tCO2e tokens have been tokenized on the carbon token exchange, of which about 2.8 million tCO2e tokens have been burned, leaving 1.0 million tCO2 tokens available for purchase on the exchange. Over these two years, the total secondary market trading turnover has been $ 21.2 million. Trading liquidity is limited to only a few types of carbon credit tokens. The prices of these most liquid tokens move in line with prices of similar carbon projects available for purchase elsewhere.
Fatih Ecer, Tolga Murat, Hasan Dınçer, Serhat YĂŒksel
Abstract Crypto assets have become increasingly popular in recent years due to their many advantages, such as low transaction costs and investment opportunities. The performance of crypto exchanges is an essential factor in developing crypto assets. Therefore, it is necessary to take adequate measures regarding the reliability, speed, user-friendliness, regulation, and supervision of crypto exchanges. However, each measure to be taken creates extra costs for businesses. Studies are needed to determine the factors that most affect the performance of crypto exchanges. This study develops an integrated framework, i.e., fuzzy bestâworst method with the Heronian functionâthe fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function (FBWMâHâFMARCOSâH), to evaluate cryptocurrency exchanges. In this framework, the fuzzy bestâworst method (FBWM) is used to decide the criteriaâs importance, fuzzy measurement of alternatives and ranking according to compromise solution (FMARCOS) is used to prioritize the alternatives, and the Heronian function is used to aggregate the results. Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness. Through case studies, some well-known cryptocurrency exchanges operating in TĂŒrkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process. The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology. âPerception of security,â âreputation,â and âcommission rateâ are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment. Further, the best score is achieved by Coinbase, followed by Binance. The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses. The findings may pave the way for investors to take appropriate actions without incurring high costs.
Abstract This study uses the Structural Factor Augmented VAR in exogenous variables (SFAVARx) approach to analyse the impact of cryptocurrency transactions on Indiaâs major macroeconomic variables. Monthly data from May 2013 to October 2021 are sourced from the Reserve Bank of India and statista.com. The current form of cryptocurrency did not have a significant impact on inflation, production, the money supply, or major interest rates. However, given the increasing marginal participation in the crypto market, these important macroeconomic variables can be adversely affected in the future. The Central Bank Digital Currency (CBDC) with features related to India is being proposed as a proactive measure.
In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.
<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.</span></p>
This paper investigates the safe haven property of Bitcoin and the main precious metals in a state of crisis. This study focuses mainly on two critical periods, namely the COVID-19 health crisis and the Russian-Ukraine conflict. To achieve this objective, we first use the DCC-GARCH model to study the dynamic correlation between the returns of oil and the main precious metals. Then, we use a bivariate specification and a Bayesian specification to estimate the TVC-VAR model. The results of this study indicate the existence of similarity between Gold and Bitcoin in hedging capabilities. In fact, both have been weak havens during the COVID-19 health crisis and strong havens during the Russian-Ukrainian war period. On the other hand, the results suggest that ruthenium and iridium yields are uncorrelated or negatively correlated with Brent yields. In this respect, investors are called upon to keep their treasury in the form of iridium and ruthenium during this period of war. Similarly, investors were required to invest in these two assets during the COVID-19 period.
Virginie Terraza, Aslı Boru, Mohammad Mahdi Rounaghi
Abstract The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification.
This paper investigates the long-run interaction between Bitcoin and Nasdaq, U.S. Dollar Index and commodities by applying weekly data from 1 January 2017 until 21 May 2023. This study uses FMOLS, DOLS and CCR methods to examine the long-run association between the variables. The results reveal a positive and significant relationship between Bitcoin and Nasdaq, as well as a similar positive association between Bitcoin and Oil prices. Notably, the U.S. Dollar Index exhibits a negative and significant impact on Bitcoin. However, results show that Gold does not have significant impact on Bitcoin. Finally, the results show that there are significant Granger causality from Nasdaq, oil and gold to Bitcoin.
Muhammad Nabil Rateb, Sameh Alansary, Marwa Khamis Elzouka, Mohamad Galal
Abstract Sentiment analysis is a powerful tool for extracting valuable insights from social media data. In this paper, more than one million tweets spanning three months (March, June, and December 2022) regarding three cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) during the Russian-Ukrainian War are considered. Two models, a convolutional neural network with long short-term memory (CNN-LSTM) and a support vector machine (SVM) with GloVe and TF-IDF features, are trained on a labeled dataset of more than fifty thousand tweets about Bitcoin labeled as (positive, negative, and neutral). A pretrained model (Pysentimento) for sentiment analysis is also employed to compare the performances of the three models. The models are tested on the labeled dataset and then evaluated on the unlabeled tweets, revealing that Pysentimento's level of accuracy outperforms the other two models. Google Trends, along with the opening and closing prices, and the volume of the three cryptocurrencies, in addition to the results of Pysentimento sentiment classification, are employed to apply the Pearson correlation coefficient and conduct price prediction analysis using the SARIMA model. It is found that Bitcoin may appeal to those seeking stability and a known record of accomplishment, while Binance Coin and Ethereum may attract investors looking for more diverse opportunities. Sentiment analysis using machine learning is found to provide invaluable information for cryptocurrency price forecasting and trading strategies, especially in the context of geopolitical events and market volatility.
Abstract The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.
Khaled Mokni, Ghassen El Montasser, Ahdi Noomen Ajmi, Elie Bouri
Abstract Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors. This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies, Bitcoin and Ethereum, and the factors that drive efficiency. It uses daily data from August 7, 2016, to February 15, 2023, the adjusted market inefficiency magnitude (AMIMs) measure, and quantile regression. The results show evidence of time variation in the levels of market (in)efficiency for Bitcoin and Ethereum. Interestingly, the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles. Notably, cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of (in) efficiency, whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient. Finally, the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles.
Dennis Koch, Vahidin Jeleskovic, Zahid Irshad Younas
This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility.
Chengying He, Yong Li, Tianqi Wang, Salman Ali Shah
Abstract In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven.
Purpose Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets. Design/methodology/approach This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set. Findings The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022. Originality/value The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
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.
This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.
The rapid rise of Bitcoin, a decentralized digital currency, has attracted significant attention from investors, researchers, and policymakers alike. The relationship between traditional stock prices and Bitcoin prices has garnered considerable attention in recent years. This research paper aims to explore the interconnections and dynamics between stock prices and Bitcoin prices by employing a Vector Autoregression (VAR) model. The study utilizes a comprehensive dataset spanning a specific time period, encompassing daily or monthly observations of stock prices and Bitcoin prices. The VAR model allows for the analysis of the joint behavior of these variables, capturing both short and long-term relationships, showing the effects of stocks on Bitcoin, but not the other way around. The research also underscores the necessity for continuous monitoring and analysis as the cryptocurrency landscape evolves rapidly. It highlights the significance of understanding the intricate dynamics between traditional financial markets and emerging digital assets, such as Bitcoin, in order to make informed investment decisions and mitigate potential risks.
Bitcoin, a pioneering cryptocurrency, has captivated the world with its volatility and price swings. Its price forecasts hold vital importance for investors, policymakers, and technologists. This article delves into the intricate domain of researching and predicting Bitcoin prices, grounded in diverse data exploration and stability assessment. The application of sophisticated predictive models further underscores the analysis, encompassing mathematics, statistics, and AI. Beyond financial gains, these forecasts impact regulatory decisions and technological advancements. This article converges multiple disciplines, bridging finance, technology, and data science to unveil Bitcoin's enigmatic behavior. This paper finds that the ARIMA Model can help predict the price of bitcoin. Itâs not just about predicting prices; it's about deciphering the potential of blockchain and reshaping our understanding of modern finance in an era of profound technological transformation. So investors should consider bitcoin as a long-term investment. The value of Bitcoin has historically appreciated over time, but short-term price fluctuations are common. Investors should avoid making impulsive decisions based on daily price movements. The second is to use reputable cryptocurrency exchanges and hardware wallets to securely store investors' bitcoins.
Several papers estimate the time series properties of bitcoin prices. However, to know that bubbles occur, an estimate of fundamental value is needed. Few estimates of bitcoinâs fundamental price exist, and these are either statistical in nature or based on questionable economic theory. This paper calculates the non-bubble dollar price of bitcoin using a modification to the quantity theory of money, and it is estimated to be about $54.00. At the November 2021 peak market price of bitcoin, the bubble component was more than 99%. Even at end-August 2023 after bitcoin prices had more than halved, the bubble component was still more than 99%.