The Liquidity of Bitcoin

Loi, Hio

doi:10.5539/ijef.v10n1p13

Cited by 23 publications

(12 citation statements)

References 6 publications

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“…Likewise, the price dynamics of Bitcoin show evidence of strong volatility and heavy tail behavior which seem to differ fundamentally from classic currencies, see for example Baur et al (2018); Osterrieder and Lorenz (2017); Carrick (2016); Kasper (2017) and Osterrieder (2017); Chan et al (2017). To some extent these peculiarities can be attributed to market liquidity which cannot compete with classic securities exchanges and which varies across Bitcoin exchanges, see (Loi, 2017). Nonetheless, Bitcoin is increasingly perceived as a viable alternative investment opportunity with potential for portfolio diversification and risk hedging, see for example Chuen et al (2017); Brière et al (2015); Dyhrberg (2016); Petukhina et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Bitcoin and market-(in)efficiency: a systematic time series approach

Bundi

Wildi

2019

Digit Finance

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Recently, cryptocurrencies have received substantial attention by investors given their innovative features, simplicity and transparency. We here analyse the increasingly popular Bitcoin and verify pertinence of the efficient market hypothesis. Recent research suggests that Bitcoin markets, while inefficient in their early days, transitioned into efficient markets recently. We challenge this claim by proposing simple trading strategies based on moving average filters, on classic time series models as well as on non-linear neural nets. Our findings suggest that trading performances of our designs are significantly positive; moreover, linear and non-linear approaches perform similarly except at singular time periods of the Bitcoin; finally, our results suggest that markets are becoming less rather than more efficient towards the sample end of the data.

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Section: Introductionmentioning

confidence: 99%

Bitcoin and market-(in)efficiency: a systematic time series approach

Bundi

Wildi

2019

Digit Finance

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“…Kim ( 2017 ) and Dyhrberg et al ( 2018 ) suggested that BTC’s attractiveness for retail trading lies in its lower transaction costs. Loi ( 2018 ) also regarded various exchanges to compare liquidity. By implementing different low-frequency liquidity indicators, the author found that BTC’s liquidity is typically lower than stocks and that liquidity differs throughout exchanges.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By closely relating to this strand of literature, we found many empirical studies documenting the linkage between trading volume and returns in the cryptocurrency market, indicating the need for exploring the determinants of trading activity (Bouri et al 2019c ). Many studies capitalized on transaction data for capturing liquidity in the cryptocurrency market while mainly focusing on BTC, such as Loi ( 2018 ), Wei ( 2018 ), and Brauneis and Mestel (2018). Overall, this literature strand has explored various facets of cryptocurrency liquidity and predominantly investigates the linkage between liquidity and efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Liquidity connectedness in cryptocurrency market

et al. 2022

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We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

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“…Apart from significant market inefficiency suggesting existence of profit opportunities, cryptocurrency markets are also known to be susceptible to bubbles, leading to frequent bear markets [5], [59]. Additionally, liquidity is frequently a concern there [60], [61]. Those factors were found to contribute positively to pairs trading performance in traditional stock markets, and cryptocurrencies are also unique in the wide-spread availability of highfrequency data, which is another area in which pairs trading appears to be successful.…”

Section: Literature Reviewmentioning

confidence: 99%

Pairs Trading in Cryptocurrency Markets

Fil

Krištoufek

2020

IEEE Access

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Pairs trading is a strategy based on exploiting mean reversion in prices of securities. Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable. We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies. In our backtests, the strategies underperform classical benchmarks. However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows. Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns-0.07% monthly, this increases to 11.61% monthly for 5-minute frequency. Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets. INDEX TERMS cryptocurrencies, intraday trading, pairs trading

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The Liquidity of Bitcoin

Cited by 23 publications

References 6 publications

Bitcoin and market-(in)efficiency: a systematic time series approach

Bitcoin and market-(in)efficiency: a systematic time series approach

Liquidity connectedness in cryptocurrency market

Pairs Trading in Cryptocurrency Markets

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