This work aims to contribute to a deeper understanding of cryptocurrencies, which have emerged as a unique form within the financial market. While there are numerous cryptocurrencies available, most individuals are only familiar with Bitcoin. This knowledge gap and the lack of literature on the subject motivated the present study to shed light on the key characteristics of cryptocurrencies, along with their advantages and disadvantages. Additionally, we seek to investigate the integration of cryptocurrencies within the financial market by applying a dynamic equicorrelation model. The analysis covers ten cryptocurrencies from June 2nd, 2016 to May 25th, 2021. Through the implementation of the dynamic equicorrelation model, we have reached the conclusion that the degree of integration among cryptocurrencies primarily depends on factors such as trading volume, global stock index performance, energy price fluctuations, gold price movements, financial stress index levels, and the index of US implied volatility.
This paper investigates the relationship between geopolitical risks (GPR) and the growth rate of Bitcoin (BTC) volume. Our analysis utilizes dynamic panel data from 33 individual countries and the European economic region. Empirical results demonstrate that GPR has a significant positive impact on BTC volume growth, particularly in developing countries. Our results are confirmed by several robustness checks, like Lagged IV, and volatility check among others. Our study offers a new perspective on BTC, as the novelty of the data used helps us understand the dynamics of BTC volume.
The global energy sector is undergoing a significant transformation, driven by the emergence of âprosumersâ - individuals who generate and consume energy. This shift is redefining traditional roles and is propelled by a growing demand for sustainable and renewable energy. Prosumers utilize decentralized energy sources, such as solar panels and wind turbines, enhancing energy independence by producing their own energy and selling any surplus back to the grid. However, this decentralized landscape presents challenges in accurately tracking carbon emissions and establishing equitable pricing mechanisms. In response to these challenges, we propose an innovative blockchain-based peer-to-peer (P2P) trading platform for carbon allowances. This novel approach gives prosumers a decisive influence over energy pricing, ensuring a more equitable distribution of energy resources. The blockchain framework benefits from decentralization, promoting transparency, security, and an immutable record of energy transactions and carbon emissions. To evaluate the platformâs effectiveness, we will initiate a real-world pilot project within the Education City Community Housing (ECCH) to gather empirical data over one year. The pilot will involve various participantsâincluding prosumers and traditional consumersâand will meticulously monitor energy production, consumption, and trading activities. By comparing this decentralized system with traditional energy models, we aim to assess its impact on carbon emissions, user satisfaction, and overall economic viability, paving the way for a sustainable energy future. ⢠Web-Based Energy and Carbon Trading Marketplace. ⢠Collect and analyze energy and carbon trading market dynamics in a residential neighborhood market. ⢠Blockchain platform to verify the feasibility of the use of a decentralized trading application.
Muhammad Mahmudul Karim, Mohamed Eskandar Shah Mohd Rasid, Abu Hanifa Md. Noman, Larisa Yarovaya
This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders. ⢠We analyze 6.5 million observations to unveil intraday asymmetric return-volatility dynamics in Bitcoin and Ethereum. ⢠The Model-Free Implied Volatility, Quantile Regression Model, and Wavelet Coherence are employed. ⢠We found that asymmetry in these relationships intensifies at lower frequencies and high quantiles. ⢠Findings contribute to cryptocurrency literature using high-frequency data across different intervals.
â KosmosCoin: Redefining Global Finance through a New Reserve Currency Paradigmâ. The concept of KosmosCoin as a global reserve currency presents a revolutionary approach to addressing the challenges and limitations of existing fiat currencies and cryptocurrencies. Unlike traditional currencies, KosmosCoin is backed by tangible assets such as land, population, and precious metals, providing inherent stability and value. This paper explores the unique selling points of KosmosCoin, including its potential to enhance economic stability, promote financial inclusion, and increase monetary sovereignty. By leveraging blockchain technology and decentralized governance models, KosmosCoin aims to create a transparent, efficient, and inclusive financial ecosystem. Key findings of this research indicate that KosmosCoin could significantly reduce transaction costs, improve liquidity, and facilitate global trade. However, practical implementation faces challenges related to scalability, security, privacy, and regulatory compliance. Despite these obstacles, the potential economic implications of KosmosCoin are profound, suggesting a promising avenue for reshaping the global financial landscape. This paper concludes that with collaborative efforts and strategic planning, KosmosCoin has the potential to become a viable and transformative global reserve currency.
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Abstract Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.
Ijaz Younis, Anna Min Du, Himani Gupta, Waheed Ullah Shah
Decentralized Finance (DeFi) assets, commodities, and Islamic stock market cointegration are affected by technological innovations, market dynamics, investor behavior, and crises. This study investigates the dynamics of returns and volatility for three DeFi assets, six commodities, and three Islamic stock markets from December 2019, to March, 2023, and identifies higher spillover effects during crises. Links among the Cross-DeFi, commodity, and Islamic markets significantly influence returns and volatility during crises. Notably, the commodities index emerged as a pivotal and substantial transmitter of risk during the Russian-Ukraine war crisis, with Emerging Markets (EM) being a key recipient. However, during the COVID-19 pandemic, livestock indices assume the role of prominent risk-return spillover receivers. The findings indicate robust returns and volatility interconnected between DeFi assets and Islamic markets with a moderate level of connectivity among commodity groups. WDI, ACWI, and EM explained 75 % of the variance observed during crisis episodes. This study formulates strategic portfolio management within and between connectedness among return volatilities by highlighting the stability of DeFi assets, the diversification potential in commodities, and a balanced option in Islamic markets. Our study provides a deep and insightful understanding of the stakeholders across markets during crises. ⢠Notable spillovers in DeFi, commodities, and Islamic markets during crises. ⢠Commodities drove risk during the Russian-Ukraine war, affecting Emerging Markets. ⢠DeFi stability, commodity diversification, and Islamic market balance guide crisis management.
The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCHâcopula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, RĂŠnyi, and Tsallis entropy measures; KolmogorovâSinai complexity; HurstâMandelbrot and Loâs R/S tests; and Phillipsâ and Geweke and Porter-Hudakâs fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCHâcopula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.
This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. Previous investigations have explored the profitability of Bitcoin, yet our research delves into its relationship with energy costs. Regarding the trade-offs, the Pareto analysis reveals that trading profit and energy cost do not strongly inversely correlate. The range of outcomes shows a relatively uniform trading profit (from 1.302,85 to 1.310,22$), but a broader variation in energy costs (from 1.141,66 to 5.657,94$). While the trading profit remains stable, there is a wide array of options for minimizing energy cost, which is influenced by various constraints and market conditions. Solutions tend to cluster more in areas of higher energy costs. However, the variability in energy costs offers Bitcoin miners choices, allowing them to tailor strategies, whether that involves prioritizing energy efficiency, profit maximization or striking a balance.
Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.
Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin , Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail.
The financial markets are undergoing rapid transformations that raise fundamental questions about the effectiveness of traditional investment models and strategies. Nowadays, investment options are incomparably wider than ever before, and one of the areas of this global financial transformation is alternative investments, so the question is what might be the trends of one of these alternative investments, non-fungible tokens (NFT). The object of the study is alternative investments, such as NFTs. The article intends to reveal how NFTs might impact the valuation and trade of digital assets, as well as to identify the key advantages and risks associated with NFTs for investors and creators. The research will carry out cluster analysis of NFTs, which will help to better understand the NFT market, learn about possible prospects and developments, possible advantages and disadvantages, as well as the level of risk.
Abstract Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.
The financial markets experienced a thrilling saga between 2020 and 2023, characterised by a series of unprecedented events and captivating dynamics that set the stage for a compelling exploration of the interaction between bitcoin prices and the S&P 500 Index. This study systematically examines the correlation between bitcoin prices and the S&P 500 Index using the Yahoo Finance dataset over a 48-month period. Using the extensive Yahoo Finance dataset and the analytical capabilities of R Statistics & R Studio, the present research covers a comprehensive period of 48 months (2020-2023). The study identifies a robust positive correlation, quantified by a correlation coefficient of 0.7726, indicating a significant alignment between bitcoin price movements and the S&P 500 index. Monthly price variables obtained from an open-source repository provide a comprehensive overview of the relative dynamics of these financial assets. This analysis provides valuable insights into the current behaviour of bitcoin and the S&P 500 index, as well as concise observations on the dynamics of their correlation.
This study employs the Maximal Overlap Discrete Wavelet Transform technique to analyze the wavelet-based correlations between Bitcoin, bond markets, and thirteen sectoral stock indices in India over the period from 2017 to 2023, focusing on the comparison of pre-and post-COVID-19 pandemic effects. The aim is to investigate the dynamic interrelationships and to understand the impact of the COVID-19 pandemic on these financial assets. The study period is divided into preCOVID-19 and post-COVID-19. Findings from the study reveal a minimal negative correlation between Bitcoin, bond markets, and the sectoral stock indices in the pre-COVID era, indicating a lack of significant interdependence among these assets. However, the scenario changes markedly in the post-COVID period, shifting towards a positive correlation. This shift suggests that the COVID-19 pandemic has altered the relationship dynamics, leading to a more interconnected financial environment where movements in Bitcoin have begun to show a significant positive correlation with the movements in bond and sectoral stock indices in India. The study contributes to the existing literature by providing empirical evidence of how external shocks, such as the COVID-19 pandemic, can influence the correlation patterns among different financial assets. It highlights the importance of considering the changing dynamics in financial market correlations for investors, policymakers, and researchers in portfolio diversification, risk management, and financial stability analysis. Further, it underscores the role of alternative investments like Bitcoin in the evolving market landscape, particularly in response to global crises.
This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.
Umar Nawaz Kayani, Mirzat Ullah, Ahmet Faruk Aysan, Sidra Nazir ¡ 5 authors
This study delves into an exploration of quantile connectedness across the domains of digital and traditional financial assets with the renewable energy prices index. The daily frequency dataset, spanning from January 02, 2018, to December 04, 2023, encapsulates diverse economic crises. Our inquiry elucidates distinctive patterns by employing empirical analyses utilizing quantile connectedness and Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodologies. In this context, DeFi assets (Chain-link) emerge as the primary recipient of information shocks, while Bitcoin distinguishes itself as the preeminent transmitter of such shocks within the network. Notably, digital assets manifest heightened volatility in contrast to traditional and energy indices. Furthermore, our findings underscore that the gaming industry, specifically focusing on Non-Fungible Tokens (NFT), presents itself as the most fitting asset for portfolio inclusion. This assertion gains credence from its comparatively lower degree of connectedness with other underlying assets. These findings have significant implications for investors and portfolio managers, furnishing valuable insights into the dynamics of asset interdependencies. Consequently, this aids in cultivating a more discerning approach to investment decision-making. ⢠Bitcoin is a significant transmitter of shocks, whereas DeFi assets like Chain-link predominantly receive them, highlighting their central roles in financial networks. ⢠Digital assets exhibit higher volatility than traditional and energy assets. The gaming industry, notably through Non-Fungible Tokens (NFTs), offers potential for portfolio diversification due to their minimal connectedness with other asset classes. ⢠The study provides critical insights into the interconnectedness of various assets, crucial for investors and portfolio managers to refine investment strategies and enhance decision-making.
Roland Akuoko-Sarpong, Stephen Tawiah Gyasi, Hannah Affram
The creation of cryptocurrencies has signified many consequences for financial markets of the traditional kind and their effectiveness. This research seeks to explore the effects of cryptocurrencies on a number of the other traditional markets in aspects of price discovery, volatility, interdependence, and information transmission. Event study analysis of everyday price changes and using multivariate cointegration analysis to cryptocurrencies and the evidence is that the cryptocurrencies are inefficient as characterized by irrational behavior, bubbles, and erratically fluctuating volatilities. However, they affect a range of currency, commodity, and stock market indexes by showing return and volatility spillover effects suggesting information flowing from one market to another. Alnet, cryptocurrency markets seem inefficient on their own but over time enhance the efficiency of linked traditional markets through participation and connectivity of global financial systems. The study contributes valuable insights into the evolving nature of financial markets in the digital era through discussions on market structure, behavioral factors, and policy implications.
Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti
We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices.
Abstract This paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlationâgeneralized autoregressive conditional heteroskedasticity (DCCâGARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be shortâlived, indicated by a relatively low DCCâGARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCCâGARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through realâworld examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the shortâlived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.
Arfan Shahzad, Yasmin Anwar, Muhammad Arif Nadeem, Waqas Shair
The advancement in technologies has changed the picture of todayâs economy. Cryptocurrency is the most trending currency nowadays. The form of cryptocurrency that is most commonly used in trading is Bitcoin. Since 2016, continuous fluctuations have been observed in the price of Bitcoin. The objective of the current study is to classify the strong predictor of Bitcoinâs price fluctuations and the associations of all these variables with each other. The price of several variables is selected as independent variables, including oil, VIX index, and US dollars. The price values for all study variables are collected for one year daily. The study findings indicated that lag 2 in the VAR model is the optimum lag for the model using HQIC and SBIC criteria, so todayâs price depends on the previous two daysâ price of independent variables. The correlation results indicated that the previous two-day price of EURO predicts the BTCâs todayâs price. A negative association is found between VIX and BTC. It is indicated that a 1 percent increase in the price of the VIX index will lead to the 60 decreases in BTCâs today price. The study also showed that it is not the price of BTC that forecasts todayâs worth of BTC, but it is the prices of VIX, euro, and oil that can predict todayâs price of BTC.
DomĂciĂĄn MĂĄtĂŠ, Hassan Raza, Ishtiaq Ahmad, SĂĄndor J. KovĂĄcs
Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.
Ijaz Younis, Muhammad Abubakr Naeem, Waheed Ullah Shah, Xuan Tang
This study analyzes the inter-dependence of the oil, gold, Bitcoin (BTC), and Gulf Cooperation Council stock markets during the recent RussiaâUkraine and IsraelâPalestine conflicts. The study found that these markets were less inter-connected during oil battles and the RussiaâUkraine conflict but more inter-connected during the COVID-19 crisis. Findings indicated that Oman, Kuwait, gold, and Qatar are the most significant spillover receivers, whereas the United Arab Emirates (UAE), Kingdom of Saudi Arabia, and West Texas Intermediate are the primary risk spillover transmitters in the IsraelâPalestine conflict. Additionally, BTC and the UAE are significant transmitters, whereas Kuwait and Qatar are the highest-risk spillover receivers in the RussiaâUkraine war. Portfolio estimates revealed that gold, BTC, and/or oil are useful in various equity markets for portfolio diversification and hedging under different market conditions and time horizons. These data can guide managers in portfolio construction and risk diversification. ⢠We examine the connectedness between oil, gold, bitcoin, and the GCC equity markets. ⢠Gold is the net recipient in all frequencies and sub-sample periods. ⢠Connectedness becomes lower in the oil battles, while higher in the COVID-19. ⢠Oil (bitcoin) is the net recipient during the oil battle periods. ⢠We estimate optimal portfolio weights and hedge ratios for portfolio strategies.
TecnolĂłgico de Estudios Superiores de Valle de Bravo, Adalberto GonzĂĄlez-Flores
The development of a monetary system that includes advancing the understanding of the factors that affect the price of cryptoassets. Halving is a unique event in the Bitcoin ecosystem that halves the reward per mined block. Studying its impact on the price would allow a better understanding of the supply and demand dynamics that determine the market value of bitcoin and other cryptocurrencies.