Blockchain Papers

Lucas Mussoi Almeida, Marcelo Perlin, Fernanda Müller

No abstract is available for this record.

Simon Rudkin, Wanling Rudkin, Paweł Dłotko

Abstract This paper tests the extent to which the ability to correctly predict subsequent bitcoin (BTC) return signs is dependent upon historic BTC return trajectories. Using topological data analysis ball mapper (TDABM), we demonstrate that the performance of random forest and logit regression models varies according to return trajectory. A novel use of TDABM as a forecast model shows that mapping historic return trajectories can also produce more accurate directional return forecasts. Our approach highlights how the predictability of BTC price change direction is dependent on return trajectories. Visualizing historic return trajectories when forming and evaluating return forecasts is imperative.

Didier Sornette, Yu Zhang

We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least 200 weeks with an exponent approximately equal to 0.9, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between 0.3 and 2.5 depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to −1.5, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time. • Age (holding time) distribution of bitcoin is significant character in bitcoin transaction. • We find that the average distribution of bitcoin holding time is a heavy-tailed power law with an exponent about 0.9. • The analysis of book-to-market and realized return shows that bitcoin traders obtain far from optimal performance. • The time-averaged transaction flow fraction has a power-law dependence as a function of age, with an exponent about 1.5. • The multifractality exists on the measure defined as the normalized number of bitcoins exchanged at a given time.

Monia Antar

Purpose This study delves into Bitcoin’s return dynamics to address its pronounced volatility, particularly in extreme market conditions. We analyze a broad range of explanatory variables, including traditional financial indicators, innovative cryptocurrency-specific metrics and market sentiment gauges. We uniquely introduce the Conference Board Leading Economic Indicator (LEI) to the cryptocurrency research landscape. Design/methodology/approach We employ quantile regression to examine Bitcoin’s daily and monthly returns. This approach captures timescale dependencies and evaluates the consistency of our findings across different market conditions. By conducting a thorough analysis of the entire return distribution, we aim to reveal how various factors influence Bitcoin’s behavior at different risk levels. The research incorporates a comprehensive set of explanatory variables to provide a holistic view of Bitcoin’s market dynamics. Additionally, by segmenting the study period, we assess the consistency of the results across diverse market regimes. Findings Our results reveal that factors driving Bitcoin returns vary significantly across market conditions. For instance, during downturns, an increase in transaction volume is linked to lower Bitcoin returns, potentially indicating panic selling. When the market stabilizes, a positive correlation emerges, suggesting healthier ecosystem activity. Active addresses emerge as a key predictor of returns, especially during bearish phases, and sentiment indicators such as Wikipedia views reveal shifting investor optimism, depending on market trends. Monthly return analysis suggests Bitcoin might act as a hedge against traditional markets due to its negative correlation with the S&P 500 during normal conditions. Practical implications The study’s findings have significant implications for investors and policymakers. Understanding how different factors influence Bitcoin returns in varying market conditions can guide investment strategies and regulatory approaches. Originality/value A novel contribution of this study is the identification of Bitcoin’s sensitivity to broader economic downturns as demonstrated by the negative correlation between LEI and returns. These insights not only deepen our understanding of Bitcoin market behaviour but also offer practical implications for investors, risk managers and policymakers navigating the evolving cryptocurrency landscape.

Md Zahidul Islam, Md. Shahidul Islam, Md Abdullah Al Montaser, Md Rasel · 7 authors

The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.

Suleiman Umar Suleiman, Kamal Tasiu Abdullahi

This study analyses the effect of cryptocurrency on the Nigerian economy. The development of crypto-currency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges, such as illicit financial flow and terrorism, amongst others, to the country. This study, therefore, sought to examine the effect of crypto-currency on the Nigerian economy. The study hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2015 to 2020. The data were analyzed using a simple regression model. The result shows that R is 7.9%, which means that there is a low positive relationship between crypto-currency and the level of economic development in Nigeria. It further shows an adjusted R square of -38.4 which depicts that crypto-currency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.945, which is higher than the set p-value of 0.05, shows that crypto-currency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of crypto-currency in Nigeria, the CBN needs to ensure that laws and mechanisms are put in place to capture the activities of crypto-currency in the country adequately.

Sibtain Syed, Syed Muhammad Umar Talha, Arshad Iqbal, Naveed Ahmad · 5 authors

Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.

Matthias Mück, Thomas Schmidl, Julian Wolf

This study investigates the efficiency of cryptocurrency markets by examining the presence and exploitability of arbitrage opportunities. Using high-frequency data from the Binance Exchange, we implement a triangular arbitrage strategy, considering Bitcoin, Litecoin, and the U.S. Dollar. We find 4,879 possible arbitrage opportunities. Although these findings suggest potential inefficiencies, transaction costs and limited trading volumes in the order book eliminate their profitability. Consequently, centralized cryptocurrency markets exhibit a high degree of efficiency. Moreover, our results suggest that the mere number of triangular arbitrage opportunities is not a reliable indicator of market inefficiency. • Triangular arbitrage opportunities at cryptocurrency exchanges do exist. • Transaction costs, potential slippage and limited trading volumes in the order book eliminate their profitability. • Centralized cryptocurrency markets exhibit a high degree of efficiency. • The mere number of triangular arbitrage opportunities is not a reliable indicator for market efficiency.

Iulia Cristina Iuga, Raluca Andreea Nerişanu, Larisa-Loredana Dragolea

This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK model and the DCC GARCH model, the study spans data from February 17, 2020, to September 30, 2024, to analyze how cryptocurrencies, categorized by their environmental impact, influence these indices. The results reveal significant volatility spillovers from both clean and dirty cryptocurrencies, with clean cryptocurrencies such as Cardano showing a stabilizing effect, while dirty cryptocurrencies like Bitcoin exhibit more pronounced and asymmetric volatility impacts on green finance indices. Furthermore, the persistent correlations identified through the DCC GARCH model highlight the dynamic relationships between cryptocurrency markets and green finance, suggesting that shocks in cryptocurrency volatility can significantly affect the financial dynamics of renewable energy investments. These insights are valuable for portfolio diversification and risk management, indicating that certain cryptocurrencies may serve as effective hedging instruments against risks in green finance. This study contributes to a deeper understanding of the interaction between digital financial assets and sustainable investments, offering practical implications for investors, financial managers, and policymakers committed to achieving Sustainable Development Goals (SDGs).

Francesco Maria De Collibus, Carlo Campajola, Guido Caldarelli, Claudio J. Tessone

We explore patterns, regularities, and correlations in the evolving landscape of Ethereum-based tokens, both ERC-20 (fungible) and ERC-721 (non-fungible) to understand the factors contributing to the rise in certain tokens over others. By applying network science methodologies, minimum spanning trees, econometric autoregressive–moving-average (ARMA) models, and the study of accumulation processes, we are able to highlight a rising centralisation process. Not only do “rich” tokens get richer, but past transactions also emerge as more reliable predictors of new transactions. Our findings are validated across different samples of tokens.

Muhammad Yousaf, Muhammad Imran Tariq, Abdul Jabbar, Syed Qaiser Jalil

This comprehensive review paper explores the diverse landscape of cryptocurrency forecasting, tracing its evolution from an alternative to traditional monetary systems to its significant growth in the global financial arena. It consolidates existing research by categorizing and analyzing 234 scholarly articles, organizing them into machine learning, deep learning, deep reinforcement learning, and statistical methodologies, and evaluating the related metrics. The case study titled “Examining the performance differences between backtesting and forward testing” highlights the challenges investors face, as strategies that appear effective in backtesting often fail in practical use. Another case study, “Social Data Exploration in Cryptocurrency Trends,” examines how social media data can provide insights into market movements and investor sentiment, revealing the impact of social trends on cryptocurrency prices. The findings section provides a detailed view, illuminating trends such as yearly publication rates, methodological distributions, input features, training/testing splits, the total number of data samples considered, and forecasting time horizons. This survey paper serves as a valuable resource, providing researchers and investors with a solid foundation for understanding and navigating the dynamic field of cryptocurrency forecasting.

Liyi Zhou, Kaihua Qin

Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocols-estimated at approximately 77 billion USD-underscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures.

Robina Iqbal, Madhia Riaz, Ghulam Sorwar, Junaid Qadir

Cryptocurrency (CRP) has grown in popularity over the last decade. Since there is no central body to control the Bitcoin (BTC) markets, they are extremely volatile. However, several similar variables that cause price volatility in traditional markets also affect cryptocurrencies. Several bubble phases have taken place in BTC prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibited several bubble phases. Among traditional methods of analysis for this volatile market, only a small number of studies focused on Machine Learning (ML) techniques. The present study objective is to get an in-depth knowledge of the time series properties of CRP data and combine volatility models with ML models. In the hybrid method, we first apply the Nonlinear Generalized Autoregressive Conditional Heteroskedasticity (NGARCH) model with asymmetric distribution to calculate standardized returns, then forecast the UP and DOWN movement of standardized returns through ML models such as Logistic Regression (LR), Linear Discrimination Analysis (LDA), Quadratic Discrimination Analysis (QDA), Artificial Neural Networks (ANNs), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The findings show that the proposed hybrid approach of time series models and ML accurately predicts prices; specifically, the KNN model reveals that the scheme can be applicable to CRP market prediction. It is deduced that ML methods combined with volatility models have the tendency to better forecast this volatile market.

Mohamad Hassan Shahrour, Ryan Lemand, Mathis Mourey

Purpose This paper examines the volatility spillover effects from traditional financial assets to cryptocurrency markets and vice versa. It aims to provide insights into the dynamic interconnectedness of these markets. Design/methodology/approach This paper employs the time-varying parameter vector autoregression technique to examine the volatility spillover among the crypto markets (across leading cryptocurrencies such as Bitcoin (BTC), USD Tether, NEAR Protocol (NEAR), Immutable and Dogecoin) and traditional financial instruments (Treasury Bills (TBILL) and Volatility Index). Findings The results reveal significant bidirectional volatility spillovers between cryptocurrencies and traditional financial assets. NEAR and BTC act as a major transmitter of volatility, both influencing others significantly (71.63 and 68.17%, respectively) and being influenced by others (54.74 and 62.3%, respectively). TBILL and Grayscale Bitcoin Trust ETF are the largest net receivers of volatility, indicating a higher dependency on other assets’ volatility. Practical implications Understanding the volatility spillover dynamics can aid investors in portfolio diversification and risk management. The findings provide actionable insights for constructing portfolios that include both cryptocurrencies and traditional financial assets, allowing for more informed investment decisions under volatile market conditions. Originality/value This paper contributes to the literature by analyzing volatility spillovers among traditional financial markets and various major cryptocurrencies. It offers a framework for assessing how shocks in one market or cryptocurrency can propagate to others, thereby enhancing the understanding of interconnectedness between markets. This understanding improves our ability to risk manage modern portfolios, which increasingly include significant alternative assets like cryptocurrencies.

Yelizaveta Vitulyova, Inabat Moldakhan, P. E. Grigoriev, Ibragim Suleimenov

It is shown that the statistics of transactions of the Ethereum cryptocurrency obeys well-defined patterns. Log dependency <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>⁡</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> of the number of users <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:math> who carried out <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> transactions with the use of Ethereum cryptocurrency during a specific month on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>⁡</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math> is a linear one with high accuracy: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:mi>ln</mml:mi><mml:mo>⁡</mml:mo><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mi>b</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mi>ln</mml:mi><mml:mo>⁡</mml:mo><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mi>a</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math> . Similar statistical patterns are obtained for bitcoin transactions. It has also been established that the behavior of the coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:math> appearing in this dependence corresponds with high accuracy to the Bass diffusion model, which describes the dynamics of innovation implementation. It is shown that after the completion of the initial stage of the implementation of the Ethereum and bitcoin cryptocurrencies (since the beginning of 2018), the values of the coefficients a and b are approaching constants. On this basis, a method is proposed for identifying space weather factors on the economic behavior of the human population. In particular, it is shown that the analysis of the cross-correlation between the ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math> for the Ethereum cryptocurrency and the Ap-index of geomagnetic activity gives an example of additional tools allowing to reveal the influence of space weather factors on the economic behavior of people on a global scale.

Ulysse Pavloff

Blockchains have sparked global interest in recent years, gaining importance as they increasingly influence technology and finance. This thesis investigates the robustness of blockchain protocols, specifically focusing on Ethereum Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks. Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e., malicious or faulty), and game-theoretic models that consider rational agents driven by incentives. We explore how incentives impact the robustness with both approaches. The thesis comprises three distinct analyses. First, we formalize the Ethereum PoS protocol, defining its properties and examining potential vulnerabilities through a distributed systems perspective. We identify that certain attacks can undermine the system's robustness. Second, we analyze the inactivity leak mechanism, a critical feature of Ethereum PoS, highlighting its role in maintaining system liveness during network disruptions but at the cost of safety. Finally, we employ game-theoretic models to study the strategies of rational validators within Ethereum PoS, identifying conditions under which these agents might deviate from the prescribed protocol to maximize their rewards. Our findings contribute to a deeper understanding of the importance of incentive mechanisms for blockchain robustness and provide insights into designing more resilient blockchain protocols.

Diogo de Prince, Emerson Fernandes Marçal, Pedro L. Valls Pereira

No abstract is available for this record.

Kamalakshi Naganna, H. Naganna

Block Chain Cryptocurrencies are playing vital role in Financial as well in all other sectors .Recently in the year 2022 battle between Russo-Ukrainian is in fact enduring battle between two countries Russia and Ukraine. Subsequent to the Russian military build-up on the Russia–Ukraine border from late 2021, the battle extended ominously when Russia propelled a complete incursion of Ukraine on 24 February 2022.Monetary problem obviously showcases a foremost role in wars, the 2022 war between Russia and Ukraine is the prime major battle with a major but role of crypto-currencies. Because Russian military forces attacked Ukraine the United States along with its partners have imposed exceptional sanctions on Russia. These situations have led to lot of queries, regarding whether crypto-currencies can be employed by Russian performers to circumvent the authorizations. In a broader sense, the Russia-Ukraine crisis has made the policymakers to resolve how to normalize digital possessions. This chapter emphasizes on how best Ukraine is able to manage the financial crisis during Ukraine –Russia war using crypto-currencies and Non-fungible tokens in terms of Military and humanity.

Darya Lapitskaya, Mustafa Hakan Eratalay, Rajesh Sharma

No abstract is available for this record.

Khaladdin Rzayev, Αθανάσιος Σάκκας, Andrew Urquhart

The network effect, measured by users’ adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovators’ component is the primary driver of the association between user adoption and cryptocurrency returns, both in-sample and out-of-sample. Next, we show that innovators’ adoption improves price efficiency, while imitators’ adoption contributes to noisier prices. Furthermore, we demonstrate that the adoption model captures significant cryptocurrency market phenomena, such as herding behaviour, more effectively, making it better suited for forecasting models in cryptocurrency pricing. These results suggest that our methodology for linking early and late adopters to market dynamics can be applied to various domains, offering a framework for future research at the intersection of operational research and financial markets.

Issam Akouaouch, Anas Bouayad

Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.

Aleksander Mercik, Barbara Będowska-Sójka, Sitara Karim, Adam Zaremba

No abstract is available for this record.

Venator Santiago, Michel Charifzadeh, Tim Alexander Herberger

Purpose This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse. Design/methodology/approach A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event. Findings While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events. Practical implications The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors. Originality/value This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.

Nrusingha Tripathy, Sarbeswara Hota, Debahuti Mishra, Subrat Kumar Nayak · 6 authors

The most well-known encrypted money, Bitcoin, has a lot of promise in the future. Investors and traders always try to find a technique to forecast cryptocurrency prices to lower their risks and boost profits. However, predicting the price of cryptocurrencies is a difficult undertaking because of their unpredictability, volatility, and mobility. Different prediction architectures have been developed by researchers using machine learning (ML), deep learning (DL), and statistical methods. In this work, predictions are made utilizing the AutoRegressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBOOST), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The historical bitcoin market data is chosen, it spans the months of January 2012 through September 2020. The LSTM model performs well when compared to other models, producing a minimal Mean Absolute Error (MAE) of 5.836 and a minimal Root Mean Squared Error (RMSE) of 7.472. Increased return on investment can be achieved by investors by making well-informed decisions on what to buy, hold, or sell. That’s particularly the case with predictions about the price of Bitcoin.