Timing of price clustering and trader behavior in the foreign exchange market: evidence from Taiwan

Liu, Hao-Chen

doi:10.1007/s12197-009-9096-0

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…• foreign exchange market, see Goodhart and Curcio (1991); Sopranzetti and Datar (2002); Westerhoff (2003); Liu (2011); Lallouache and Abergel (2014);…”

Section: Price Clusteringmentioning

confidence: 99%

“…Another studies analyzed the panel data by pooled regression (see, Chung et al, 2005;Liu and Witte, 2013;Blau and Griffith, 2016), models with fixed effects (see, Box and Griffith, 2016;Blau, 2019;Das and Kadapakkam, 2020), or random effects (see, Ohta, 2006). Part of the literature models price clustering as a binary variable using logit (see, Aitken et al, 1996;Brown and Mitchell, 2008;Bharati et al, 2012), or probit models (see, Kahn et al, 1999;Sopranzetti and Datar, 2002;Palmon et al, 2004;Ohta, 2006;Alexander and Peterson, 2007;Capelle-Blancard and Chaudhury, 2007;Liu, 2011;Narayan and Smyth, 2013;Lien et al, 2019). Blau (2019) estimated a vector autoregressive process and examined the impulses of price clustering in response to an exogenous shock to investor sentiment.…”

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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“…• foreign exchange market, see Goodhart and Curcio (1991); Sopranzetti and Datar (2002); Westerhoff (2003); Liu (2011); Lallouache and Abergel (2014);…”

Section: Price Clusteringmentioning

confidence: 99%