An empirical study of risk diffusion in the cryptocurrency market based on the network analysis

Yang, Mingyuan; Wu, Zhenguo; Wu, X.

doi:10.1016/j.frl.2022.103180

Cited by 9 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the cryptocurrency *Address for correspondence: Haifeng Wu, E-mail: haifengwu@mail.nankai.edu.cn. Institute of Finance, School of Economics, Nankai University, Tianjin, PR China market has seen even more explosive growth: the price of Bitcoin has soared from an initial $0.0025 to as high as $68,928.90 (Howson and Vries, 2022); 23,000 different types of cryptocurrencies, such as ethereum (ETH), ethereum classic (ETC), and Litecoin (LTC), have emerged, and the total cryptocurrency market capitalization exceeds $2.6 trillion, with an economic value of more than $150 billion (Yang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: The Impact of FOMC Announcements on Cryptocurrency Risk Spillover in Different Market Conditions

2024

Preprint

View full text Add to dashboard Cite

This paper investigates the impact of FOMC announcements on cryptocurrency risk spillovers under different market conditions and their mechanisms of action. We calculate the high-frequency RGARCH volatilities of four major dirty cryptocurrencies and six major clean currencies, and apply the quantile-expanded joint-network approach to test their risk spillovers under different market conditions, and then investigate their impact and mechanism of action by monetary shocks. The results show that: (i) The total spillover in the extreme volatility market is larger than the total spillover in the normal market, and there is a tail asymmetry. (ii) The total cryptocurrency risk spillover in the highly volatile market, the normal market, and the smooth volatility market are all positively affected by monetary shocks. (iii) Mechanism analysis shows that the FOMC announcement will have an impact on the total cryptocurrency market risk spillover through two channels, which are changing the market expectations and hedging demand. Heterogeneity analysis shows that clean cryptocurrencies have positive net spillovers to dirty cryptocurrencies, and the mutual spillovers between the two are significantly affected by monetary shocks in extremely volatile markets.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: The Impact of FOMC Announcements on Cryptocurrency Risk Spillover in Different Market Conditions

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Several studies have investigated quantile connectedness, risk diffusion, contagion, and tail spillover in crypto-markets (Bouri et al 2021 ; Jena et al 2020 ; Mensi et al 2021b ; Xu et al 2021 ; Naeem et al 2022 ; Yang et al 2022 ) and commodity markets (Umar et al 2021b , c ; Umar et al 2022c ; Hanif et al 2023 ). In parallel, both connectedness and elevated uncertainty in traditional financial markets have been widely studied recently (Bams et al 2017 ; Godil et al 2020 ; Roh et al 2020 ; Zhang et al 2021 ; Benlagha and El Omari 2022 ; Mensi et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets

Mensi

Gubareva

et al. 2023

Financ Innov

View full text Add to dashboard Cite

This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.

show abstract

“…For each of these protocols, the potential stake distribution lag is several days, which they discovered after surveying existing secure PoS proposals. Yang et al [26] examine the diffusion of risk in the cryptocurrency market from 2018 to 2021 and compare it to the stock and foreign exchange markets, finding that the risk diffuses more easily in the cryptocurrency market and is most affected by cryptocurrency with large market capitalizations or low-turnover. George et al [27] performed a study on the distributed operating system (DOS) for permissioned blockchains.…”

Section: Literature On Related Applications Of Blockchain Technologymentioning

confidence: 99%

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

2023

View full text Add to dashboard Cite

TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.

show abstract

An empirical study of risk diffusion in the cryptocurrency market based on the network analysis

Cited by 9 publications

References 28 publications

RETRACTED: The Impact of FOMC Announcements on Cryptocurrency Risk Spillover in Different Market Conditions

RETRACTED: The Impact of FOMC Announcements on Cryptocurrency Risk Spillover in Different Market Conditions

Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets

TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

Contact Info

Product

Resources

About