Purpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoinâs volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoinâs price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoinâs volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy. Keywords: Bitcoin, ARCH models, GARCH Models, forecasting, ARIMA models JEL Codes: C58, G10, G12
This study investigates the symmetric and asymmetric connectedness of recently developed disruptive assets â cryptocurrency and important conventional assets â Real Estate Investment Trusts (REITs) across various time scales and frequencies. We measure the strength and magnitude of returns and volatility spillover between REITs and cryptocurrency using both time series and machine learning approaches. Our findings support the dominance of conventional assets in the correlation with disruptive assets, demonstrated by REITs price movementsâ destabilization of the correlation network. Interestingly, this destabilization is concentrated in bear markets, suggesting that while investors trade both cryptocurrencies and REITs in bull markets, investors are likely to hedge their cryptocurrency investments with REITs or avoid cryptocurrencies in bear markets. Our empirical findings provide implications for both asset allocation and policy making.
Carlos Esparcia, Tarek Fakhfakh, Francisco JareĂąo, Achraf Ghorbel
Abstract This study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.
Jan Ĺ Ăla, EvĹžen KoÄenda, Ladislav KriĹĄtoufek, JiĹĂ KukaÄka
Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history . Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.
January 2024 which covered recent catastrophic events such as Bitcoin Price Crash-2018, COVID-19, Global plummet in Oil Demand-2020, and Russia-Ukraine War. The findings reveal that NFTs and DeFi assets possess weak connectedness with housing market in normal market state, however, connectedness become robust in extreme bearish and bullish market states. Moreover, NFTs and DeFi assets are net transmitters and housing market acts as net receiver of shock in all market states. Investors, portfolio managers, and policymakers should carefully analyze both digital financial assets and housing market especially during extreme events to build well diversified profit-gaining portfolios and to formulate policies.
Richard Umeokwobi, Edmund Tamuke, Obumneke Ezie, Marvelous Aigbedion ¡ 5 authors
The rapid adoption and growing prominence of Bitcoin and other cryptocurrencies have sparked significant interest and debate among economists, policymakers, and financial analysts. In Sub-Saharan Africa, where traditional financial systems often face challenges such as limited access to banking services, high transaction costs, and volatile currencies, Bitcoin presents both opportunities and risks. Understanding the interplay between Bitcoin and key monetary indicators such as monetary aggregates, exchange rates, and interest rates can provide valuable insights for policymakers and stakeholders in these economies. This study therefore seeks to investigate the nexus between monetary indicators and Bitcoin in selected Sub-Saharan African countries using a Panel ARDL (Autoregressive Distributed Lag) approach. The analysis focuses on understanding the dynamic relationship between key monetary variables, such as monetary aggregates, exchange rates, interest rates, and Bitcoin prices, from 2010 quarter three to 2022 quarter four. The findings reveal several significant relationships between monetary indicators and Bitcoin across the selected Sub-Saharan African countries. In the short run of the Panel Ardl monetary aggregates exhibit a positive relationship with Bitcoin prices, indicating that changes in the money supply may influence the demand for cryptocurrencies. Conversely, both exchange rates and interest rates show a negative relationship with Bitcoin prices in the short run, suggesting that currency depreciation and higher borrowing costs may reduce demand for Bitcoin. In the long run, the relationship between monetary aggregates and Bitcoin remains positive, emphasizing the potential influence of money supply on cryptocurrency markets over time. However, the significance of exchange rates diminishes, indicating a less pronounced impact in the longer term. Interestingly, interest rates continue to exhibit a significant negative relationship with Bitcoin prices in the long run, highlighting the persistent effect of borrowing costs on cryptocurrency demand. These results have important implications for policymakers, investors, and researchers interested in the intersection of monetary policy and cryptocurrency markets in Sub-Saharan Africa. Policymakers may consider the impact of monetary policy decisions on cryptocurrency adoption and market dynamics, while investors can use these insights to inform their investment strategies.
Haider Ali, Muhammad Aftab, Faheem Aslam, Paulo Ferreira
Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFAâespecially the fluctuation function, the varying Hurst exponent, and the Renyi exponentâconfirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.
Christian Kreuzer, Ralf Laschinger, Christopher Priberny, Sven Benninghoff
Cryptocurrencies provide an escape from the conventional financial system and its regulations and could therefore become increasingly popular in the midst of geopolitical uncertainties. We analyze the linkage of the RussiaâUkraine conflict and the trading volume of 16 major cryptocurrencies via event study methodologies, based on a geopolitical risk index. The results show that the trading volume of most cryptocurrencies is positively affected by the events of the conflict. This is especially true for payment tokens and most utility coins. Interestingly, stablecoins show only fewer trading volumes before the actual event. Among utility tokens, Ripple in particular is positively influenced. ⢠We examine how the RussiaâUkraine conflict affects the trading volume of 16 major cryptocurrencies. ⢠Most cryptocurrencies see temporary increased trading volumes on events of the conflict. ⢠Payment tokens and many utility coins, in particular, experience higher trading volumes. ⢠Stablecoins only have lower trading volumes before the event.
Abstract Within the adaptive market hypothesis (AMH) framework, this study explores the dynamic impact of cryptocurrency heists on Bitcoin's market efficiency. By analysing Bitcoin's oneâminute price data, we calculate permutation entropy to assess market disorder and employ the complexityâentropy causality plane to quantify structural changes in the market. The analysis focuses on the market efficiency changes the day before, the day of, and the day after a heist, revealing that heists significantly disrupt market efficiency. Specifically, on the day of and following a heist, we observe a marked decrease in permutation entropy alongside a significant increase in complexity, indicating a notable decline in market efficiency. Further analysis shows that when a heist targets a specific token, this token draws investor attention, causing a less severe drop in Bitcoin's market efficiency, while the affected token's market efficiency drops more dramatically. These findings suggest that different token markets react differently to heists, and investors should consider adjusting their strategies to respond to these changes. For policymakers, the results highlight the critical need to enhance market stability and security through informed policy measures to mitigate the impact of such disruptive events.
Research background: The research employs the Cross-Sectional Absolute Deviation of returns (CSAD) model, augmented with modifications by Chiang and Zheng (2010) to address asymmetric investor behavior, facilitating the detection of herding behavior. Additionally, the study leverages Quantile Regression (QR), demonstrated by Barnes and Hughes (2002) to effectively capture extreme values in financial data with fat tails or skewed distributions. This approach is particularly relevant in the context of the volatile cryptocurrency market, allowing for the analysis of outliers and the assessment of the magnitude of return impacts using T-stat and Quantile Process Estimates. Purpose of the article: This study primarily centers its empirical analysis on identifying market-wide herding behavior (Henker et al., 2006) within the cryptocurrency market, spanning from January 1, 2016, to February 1, 2019, juxtaposed with the period from January 1, 2019, to January 7, 2022. The selected time frames were chosen to evaluate potential shifts in herding dynamics within this market, particularly during its phases of rapid expansion and subsequent stagnation. Methods: The Cross-Sectional Absolute Deviation (CSAD) methodology, as proposed by Chiang and Zheng (2010), was employed for herding detection, alongside the incorporation of dummy variables to discern the market conditions under which herding occurs. Herding behavior manifests when dispersion diminishes, or its increase is less than proportionate to market returns, indicating an inverse correlation between market returns and dispersion in the presence of herding. Additionally, CSAD estimation was conducted utilizing quantile regression to encompass a broader range of quantiles, facilitating the identification of herding tendencies across various return magnitudes. To delve further into investor behavior, Bitcoin was utilized as an illustrative example, elucidating investor reactions to market bubbles through the application of the Hodrick-Prescott (HP) Filter. Findings & value added: The findings reveal instances of herding behavior during downward market movements and at higher return levels preceding 2019. However, post-2019, herding is observed during upward market movements and at medium to higher return levels. This study presents compelling evidence of herding phenomena coinciding with the bursting of bubbles, particularly concerning Bitcoin. The findings provide a deeper understanding of how herding manifests differently across distinct market conditions and timeframes, offering actionable insights for investors and policymakers navigating the volatile cryptocurrency landscape. Additionally, by highlighting the correlation between herding behavior and market bubbles, particularly in the context of Bitcoin, this study contributes to the broader discourse on cryptocurrency market dynamics.
Purpose The purpose of the study is to examine the impact of uncertainty and return of classical financial assets on herding behaviour in the cryptocurrency market. Also, herding in this market and the impact of the COVID-19 pandemic have been investigated. Design/methodology/approach The study uses quantile regression to estimate the models. Daily data from ten major cryptocurrencies, the CCI30 index and three volatility indices (VIX, EVZ and GVZ), spot gold price, the MSCI and the US dollar indices from January 2018 to December 2023 have been used. Findings The findings show evidence of anti-herding during periods of simultaneous high volatility in stock and currency markets, as well as in the gold and currency markets. However, the results support herding in the whole sample period, which reduces when including the COVID-19 pandemic effect. In addition, the study does not support the relationship between returns of traditional financial assets and herding in the cryptocurrency market. Practical implications The result of the study can be useful for investors, particularly the managers of the novel class of ETFs, to make their investment decisions more consciously, regarding uncertainty in other financial markets. Also, the findings provide some insight to regulators regarding the herding behaviour in the cryptocurrency market and its influences on the financial systemâs stability. Originality/value To the best of the authorsâ knowledge, for the first time, this study examines the impact of concurrent high uncertainty conditions in classical financial markets on herding behaviour in the cryptocurrency market.
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.