Cryptocurrencies and stablecoins: a high-frequency analysis

Barucci, Emilio; Moncayo, Giancarlo Giuffra; Marazzina, Daniele

doi:10.1007/s42521-022-00055-9

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…In particular, they advise that researchers use trade data obtained from the crypto exchanges, rather than non-trade data from coin-ranking websites and other sources where data quality is significantly lower. Barucci et al, (2022) highlight that for intraday settings, cryptocurrencies quoted against BTC, ETH or Tether (USDT) are more liquidity and therefore tend to be more accurate than those quoted against the dollar. In fact, USDT facilitates trades in cryptocurrencies as fees are lower and no bank transfers are needed.…”

Section: Datamentioning

confidence: 95%

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

2023

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Section: Datamentioning

confidence: 95%

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

2023

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What drives cryptocurrency returns? A sparse statistical jump model approach

2023

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We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.

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Market impact and efficiency in cryptoassets markets

Barucci,

Giuffra Moncayo,

Marazzina

2023

Digit Finance

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We analyze markets for cryptoassets (cryptocurrencies and stablecoins), investigating market impact and efficiency through the lens of the market order flow. We provide evidence that markets where cryptoassets are exchanged between themselves play a central role on price formation and are more efficient than markets where cryptocurrencies are exchanged with the US dollar. For the first set of markets we observe some evidence of the presence of insiders/contrarians, instead in the latter we observe the predominance of herding and trend-followers.

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Cryptocurrencies and stablecoins: a high-frequency analysis

Cited by 5 publications

References 41 publications

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

What drives cryptocurrency returns? A sparse statistical jump model approach

Market impact and efficiency in cryptoassets markets

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