Follow blockchain research across journals, conferences, and preprint repositories.
3,636 results · page 29 of 152
Yan Chen, Lei Zhang, Elie Bouri
No abstract is available for this record.
Naomi A. Arnold, Peijie Zhong, Cheick Tidiane Bâ, Benjamin A. Steer · 8 authors
Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.
Anupam Dutta
Abstract The main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.
Inés Jiménez, Andrés Mora‐Valencia, Javier Perote
No abstract is available for this record.
Werner Kristjanpoller, Ramzi Nekhili, Elie Bouri
No abstract is available for this record.
Mona Mohamed, Mona Gharib
The popularity of cryptocurrencies has skyrocketed in the last several years due to the introduction of blockchain technology (BCT). Herein, we are navigating the intersection of sustainable market investment and cryptocurrency predictive analysis against the backdrop of a dynamic and evolving financial landscape marked by the surge of digital assets. This study's goal is to construct the predictive analysis model (PAM) which incorporates Long Short-Term Memory (LSTM) capabilities to predict the price of Bitcoin with high accuracy the next day and to identify the variables that influence price. In constructed PAM, we are using a comprehensive methodology to study temporal correlations within minute-by-minute bitcoin data using preprocessing, sophisticated machine learning algorithms, and data exploration. Our findings demonstrate the effectiveness of the LSTM model in forecasting bitcoin behavior, offering detailed information that is essential for long-term market investing.
Erdong Chen, Mengzhong Ma, Zixin Nie
In this paper, we analyze traders' behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance.
Eugene Msizi Buthelezi
No abstract is available for this record.
Parthajit Kayal, Sumanjay Dutta
No abstract is available for this record.
Simona‐Vasilica Oprea, Irina Georgescu, Adela Bârã
No abstract is available for this record.
Ting Gao, Huaiming Wang, Dongying Du
No abstract is available for this record.
Stanislaw P. Stawicki
Cryptocurrency investment approaches continue to evolve rapidly. Traditionally, cryptocurrency holders tend to actively support up to several distinct projects, focusing their selection criteria on specific project characteristics, project team and community, existing markets and liquidity levels, as well as the perception of each unique project’s broadly understood “mission and vision” and “future potential.” In this chapter, we will explore an index-based investment strategy as an alternative to the more traditional single- or oligo-asset approaches. In the index-based paradigm, multi-asset strategy involves equalization and redistribution of risk exposure across multiple, pre-vetted portfolio positions. This strategy, novel to the cryptocurrency space, also involves risk reduction through cost averaging, dilution of cyber security-related risk(s), as well as mitigation of liquidity restrictions related to individual-position market liquidity characteristics. Additional discussion of software platforms, including both custodial and non-custodial wallets, and the associated risk-benefit considerations, will also be included in this manuscript.
Providence Mushori, Delson Chikobvu
This paper analyses the extreme tail behaviour of Bitcoin returns by fitting a Generalised Pareto Distribution (GPD). The GPD is used to model the extreme daily Bitcoin returns over the period 2008 to 2023. The returns above the chosen thresholds, for both Bitcoin gains and losses, are selected. The GPD is then fitted to the selected excess returns. The Anderson Darling (AD) and Kolmogorov Smirnov (K-S) goodness-of-fit tests reveal that the GPD captures the distribution of the Bitcoin excess returns. The Value at Risk (VaR) and Expected Shortfall (ES) under the GPD are used to measure the extreme tail risk of the Bitcoin returns. The upside risk (gains) is found to outweigh downside risk (losses), and this gives insight to investors interested in Bitcoin.
Dhoha Mellouli, Imen Zoglami
Purpose: This paper pioneers exploring the relationship between cryptocurrencies, considering the case of non-fungible tokens (NFTs) and traditional cryptocurrencies Design/Methodology/Approach: The analysis is performed through an innovative TVP-VAR frequency connectedness approach, revealing a substantial level of dynamic integration and return transmission among cryptocurrencies systems. Findings: Our findings are multifaceted. Firstly, that there is higher total connectedness in the bearish and bullish market conditions compared to normal conditions. Secondly, the degree of connectedness is even stronger during tranquil and turbulent times such as the Covid-19 pandemic and the Russian-Ukrainian war. Thirdly, the network's net transmission behavior is predominantly by the short-term dynamics for NFT and by the long-term dynamics for Conventional cryptocurrencies, and assets' roles as net-transmitter and net-receiver can change over time. Practical Implications: These findings inform investors, traders, and portfolio managers to prioritize risk management during high-risk periods, such as COVID-19 and the Russian-Ukrainian conflict, as crises involve non-diversifiable systematic risks, demanding careful risk mitigation. Originality/Value: One of the main challenges of cryptocurrencies is determining the nature of the dynamics of their connectivity. The originality and the value of this research is to investigate whether cryptocurrencies evolve in a similar manner to each other.
Cy Watsky, Jeffrey S. Allen, Hamzah Daud, Jochen Demuth · 7 authors
Stablecoins are increasingly important in decentralized finance (DeFi) and crypto asset markets, and their prominence has led to greater scrutiny of their unique role as expressions of the U.S. dollar running on blockchain networks. Stablecoins attempt to perform a mechanically complex function – to remain pegged to the dollar, even during periods of market volatility.
Nezir Köse, Hakan YILDIRIM, Emre Ünal, Boqiang Lin
Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.
Jiexian Liu
No abstract is available for this record.
Tonuchi E. Joseph, Atif Jahanger, Joshua Chukwuma Onwe, Daniel Balsalobre‐Lorente
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 Morocco’s 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.
Jian Zhang, Jinsong Zhao, Chi‐Chuan Lee
This research examines the causal relationships between cryptocurrency uncertainty, the price of crude oil, and the price of gold using weekly data from 30 December 2013, to 21 February 2021, applying Granger-causality analysis on each quantile. Under this approach, we are able to distinguish between median and tail relationships for conditional quantiles. We find a bidirectional causal relationship between cryptocurrency uncertainty and crude oil prices, implying that crude oil price volatility is one source of cryptocurrency uncertainty, whereas a causal relationship from cryptocurrency uncertainty or crude oil prices to gold suggests that gold hedges cryptocurrency uncertainty and crude oil price shocks. Our research calls on governments to maintain cryptocurrency market stability to reduce market volatility in crude oil prices. Investors and fund managers can consider adding gold assets to portfolios that contain cryptocurrencies or crude oil in order to hedge against the risks of cryptocurrency uncertainty and crude oil price volatility.
Divya Goswami, Balraj Verma
Decentralized autonomous organizations (DAOs) represent a novel technology progress that could potentially challenge conventional organizations in terms of management and making choices. This chapter provides an introduction to decentralized finance (DeFi), situates DeFi within the framework of the conventional financial industry, establishes a connection of peer-to-peer transactions, and concludes with a discussion on policy implications. Decentralization has the capacity to weaken conventional mechanisms of accountability and diminish the efficacy of established financial regulations and enforcement. This study presents a thorough analysis of the current status of research on DAOs, highlighting the most important research areas and relevant works in the subject. Furthermore, it examines the performance of prominent decentralized finance in relation to these research areas, providing valuable observations on their real-world implementations and efficacy.
Νikolaos Kyriazis, Shaen Corbet
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.
Yue Fa Zhou, Fei Huang
No abstract is available for this record.
Manisha Yadav
Purpose The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns. Design/methodology/approach The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models. Findings This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors. Originality/value The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.
Nrusingha Tripathy, Sarbeswara Hota, Debahuti Mishra, Pranati Satapathy · 5 authors
Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies.