How stable are stablecoins?

Hoang, Lai T.; Baur, Dirk G.

doi:10.1080/1351847x.2021.1949369

Cited by 50 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 10, we provide the correlation between trading volume in different markets. As observed in Hoang and Baur (2021), trading volumes in different markets are strongly correlated, however, we observe lower correlation (than other markets) in trading volume between the USDT-USD market and the other markets. In Table 11, we provide the correlations of the realized volatility in different markets.…”

Section: Market Relationshipssupporting

confidence: 57%

“…The strong correlation between stablecoins and BTC daily volatility detected in Hoang and Baur (2021) is confirmed, the highest level is observed for USDT, see Table 16 in the Supplementary material for other stablecoins.…”

Section: Market Relationshipssupporting

confidence: 55%

“…Notice that the market for the conversion of Tether in USD is uncorrelated with the other markets. Differently from Hoang and Baur (2021), the correlations among minor stablecoins and Bitcoin are rather small, see Table 15 in the appendix.…”

Section: Market Relationshipsmentioning

confidence: 89%

See 2 more Smart Citations

Cryptocurrencies and Stablecoins: a high frequency analysis

2021

View full text Add to dashboard Cite

Section: Market Relationshipssupporting

confidence: 57%

Section: Market Relationshipssupporting

confidence: 55%

See 1 more Smart Citation

Cryptocurrencies and Stablecoins: a high frequency analysis

2021

View full text Add to dashboard Cite

“…Given the extreme volatility of Bitcoin, investors may rather need a safe haven against Bitcoin (Hoang and Baur, 2021). Grobys et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Spillovers between cryptocurrencies, gold and stock markets: implication for hedging strategies and portfolio diversification under the COVID-19 pandemic

Lamine,

Jeribi,

Fakhfakh

2023

JEFAS

View full text Add to dashboard Cite

PurposeThis study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.Design/methodology/approach The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.FindingsGenerally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.Originality/value The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

show abstract

“…Its exchange rate with U.S. Dollar maintains very close to one, so in many senses it is viewed as a "stable coin" and plays an important role in cryptocurrency trading. [5] Among digital asset exchanges, Binance has one of the best market depth for most major cryptocurrencies and also a considerable active user base. These ensures that the best representation of digital asset price movement and the robustness of trained models.…”

Section: B Contributionsmentioning

confidence: 99%

A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data

Zhao

2020

Preprint

View full text Add to dashboard Cite

This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies based on a trailing window. It is the first to use trade-by-trade data to predict shortterm price changes in fixed time horizons. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accurancy) on out-of-sample test periods. In a realistic trading simulation setting, the prediction made by the model could be easily monetized. Moreover, this study shows that the LSTM model could extract universal features from tradeby-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data. This study exceeds existing researches in term of the scale and precision of data used, as well as the high prediction accuracy achieved.

show abstract

How stable are stablecoins?

Cited by 50 publications

References 23 publications

Cryptocurrencies and Stablecoins: a high frequency analysis

Cryptocurrencies and Stablecoins: a high frequency analysis

Spillovers between cryptocurrencies, gold and stock markets: implication for hedging strategies and portfolio diversification under the COVID-19 pandemic

A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data

Contact Info

Product

Resources

About