Price clustering in Bitcoin market—An extension

Li, Xin; Li, Shenghong; Cai, Xu

doi:10.1016/j.frl.2018.12.020

Cited by 24 publications

(12 citation statements)

References 30 publications

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“…Different studies concluded that price barriers or at least significant deviations from uniformity also exist in other asset classes such as exchange rates (De Grauwe and Decupere, 1992;Mitchell and Izan, 2006), bonds (Burke, 2001), commodities (Aggarwal and Lucey, 2007;Lucey and O'Connor, 2016), derivatives (Schwartz, Van Ness and Van Ness, 2004;Chen and Tai, 2011;Jang et al, 2015;Dowling, Cummins and Lucey, 2016;Palao and Pardo, 2018) and cryptocurrencies (Fonseca, Pacheco and Lobão, 2020;Li, li and Chong, 2020). Overall, evidence of price barriers in various asset classes seems to be fairly robust.…”

Section: Previous Findingsmentioning

confidence: 99%

Do psychological barriers exist in Latin American stock markets?

Lobão

Fortuna

Silva

2020

RAE

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We examine for the first time the major stock markets of eight Latin American countries for indication of psychological barriers at round numbers. We test for uniformity in the trailing digits of the indices and use regression and GARCH analysis to assess the differential impact of being above or below a possible barrier. The Chilean stock market seems to be significantly different from its counterparts as it is the only one that showed virtually no signs of psychological barriers. There is mild to strong evidence of barriers in the remaining markets. These findings challenge the notion that most Latin American markets are unpredictable and lend credit to the claim that technical analysis strategies can be useful in some of these markets.

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Section: Previous Findingsmentioning

confidence: 99%

Do psychological barriers exist in Latin American stock markets?

Lobão

Fortuna

Silva

2020

RAE

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“…The first academic paper on the price clustering was written by Osborne (1962), where the author described the price clustering phenomenon as a pronounced tendency for prices to cluster on whole numbers, halves, quarters, and odd one-eighths in descending preference, like the markings on a ruler. Since then, there have been many studies focusing on this phenomenon -from Niederhoffer (1965) to very recent papers of Li et al (2020); Song et al (2020); Das and Kadapakkam (2020) -showing that price clustering is remarkably persistent across various markets:…”

Section: Price Clusteringmentioning

confidence: 99%

“…In recent years, great part of the research on price clustering focuses on Bitcoin due to its enormous price surge in 2017, see Urquhart (2017); Hu et al (2019); Baig et al (2019); Mbanga (2019); Li et al (2020).…”

Section: Price Clusteringmentioning

confidence: 99%

“…From then, several high-frequency analysis arises, e.g. Li et al (2020) analyzed clustering for Bitcoin prices of various time frames with the highest frequency equal to 1 minute. However, despite the frequency of the data, the majority of studies are based on rather descriptive statistics and basic linear regression analysis.…”

Section: Price Clusteringmentioning

confidence: 99%

“…Except purely theoretical study Song et al (2020), that on the other hand lacks the practical applications of their model, the literature uses well-known basic models for the empirical examination if any. The common approach is to use a linear regression model estimated by ordinary least squares method (see, Ball et al, 1985;Harris, 1991;Hameed and Terry, 1998;ap Gwilym et al, 1998b;Kandel et al, 2001;Chung et al, 2002;Cooney et al, 2003;Schwartz et al, 2004;Ahn et al, 2005;Chung and Chiang, 2006;Ikenberry and Weston, 2008;Chiao and Wang, 2009;Palao and Pardo, 2012;ap Gwilym and Verousis, 2013;Brooks et al, 2013;Davis et al, 2014;Urquhart, 2017;Hu et al, 2017Hu et al, , 2019Baig et al, 2019;Li et al, 2020), or its modifications such as two-stage least squares (see, Mishra and Tripathy, 2018), three-stage least squares (see, Chung et al, 2004Chung et al, , 2005Meng et al, 2013), or robust regressions that eliminate gross outliers (e.g. Mbanga (2019)).…”

Section: Price Clusteringmentioning

confidence: 99%

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Modeling Price Clustering in High-Frequency Prices

Holý¹,

Tomanová²

2021

Preprint

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The price clustering phenomenon, i.e. an increased occurence of specific prices, is widely observed and well-documented for various financial instruments in various financial markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behaviour, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has positive impact on price clustering.

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