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Tick size reduction and price clustering in a FX order book

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  • Mehdi Lallouache

    (FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

  • Frédéric Abergel

    (FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)

Abstract

Using a new high frequency quality data set we provide a precise empirical study of the interdealer spot market. We check that the main stylized facts of financial time series are valid for the FX market: fat-tailed distribution of returns, aggregational normality and volatility clustering. We report two standard microstructure phenomena: microstructure noise effects in the signature plot and the Epps effect. We find an unusual shape for the average book, the spread distribution being bimodal. We construct the order flow and analyse its main characteristics: volume, placement, arrival intensity and sign. Many quantities have been dramatically affected by the decrease of the tick size in March 2011. We argue that the coexistence of manual traders and algorithmic traders, who react differently to the new tick size, leads to a strong price clustering in all types of orders and affects the price formation.

Suggested Citation

  • Mehdi Lallouache & Frédéric Abergel, 2014. "Tick size reduction and price clustering in a FX order book," Post-Print hal-01006414, HAL.
    • Handle: RePEc:hal:journl:hal-01006414
    DOI: 10.1016/j.physa.2014.09.016
    Note: View the original document on HAL open archive server: https://hal.science/hal-01006414
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    References listed on IDEAS

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    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios, 2020. "Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&P500 industrial sectors with a comparison," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Vladim'ir Hol'y & Petra Tomanov'a, 2021. "Modeling Price Clustering in High-Frequency Prices," Papers 2102.12112, arXiv.org, revised Mar 2021.
    3. Xiao, Xijuan & Yamamoto, Ryuichi, 2020. "Price discovery, order submission, and tick size during preopen period," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
    4. Li, Xin & Li, Shenghong & Xu, Chong, 2020. "Price clustering in Bitcoin market—An extension," Finance Research Letters, Elsevier, vol. 32(C).
    5. repec:wsi:acsxxx:v:21:y:2018:i:08:n:s0219525918500194 is not listed on IDEAS
    6. Damien Challet & R'emy Chicheportiche & Mehdi Lallouache & Serge Kassibrakis, 2016. "Statistically validated lead-lag networks and inventory prediction in the foreign exchange market," Papers 1609.04640, arXiv.org, revised Jul 2018.
    7. Mahmoodzadeh, Soheil & Gençay, Ramazan, 2017. "Human vs. high-frequency traders, penny jumping, and tick size," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 69-82.

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