Reconstruction of Order Flows using Aggregated Data

Ioane Muni Toke ()
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Ioane Muni Toke: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, CREST - Core Research for Evolutional Science and Technology - JST - Japan Science and Technology Agency, ERIM - Equipe de Recherche en Informatique et Mathématiques - UNC - Université de la Nouvelle-Calédonie

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Abstract: We investigate TRTH tick-by-tick data on three exchanges (Paris, London and Frankfurt) and on a five-year span. A simple algorithm helps the synchronization of the trades and quotes data, enhancing the basic procedure. The analysis of the performance of this algorithm turns out to be a a forensic tool assessing the quality of the database: significant technical changes affecting the exchanges are tracked through the data. Moreover, the choices made when reconstructing order flows have consequences on the quantitative models that are calibrated afterwards on such data. Finally, this order flow reconstruction provides a refined look at the Lee-Ready procedure and its optimal lags. Findings are in line with both financial reasoning and the analysis of an illustrative Poisson model.

Keywords: Limit order book; order flow; trades and quotes matching; trade signature; Lee-Ready algorithm; model calibration (search for similar items in EconPapers)
Date: 2016
Note: View the original document on HAL open archive server: https://centralesupelec.hal.science/hal-01705074v1
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Citations: View citations in EconPapers (8)

Published in Market microstructure and liquidity, 2016, 2016-11-03, 02 (02), ⟨10.1142/S2382626616500076⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01705074

DOI: 10.1142/S2382626616500076

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