Linear models for the impact of order flow on prices. I. History dependent impact models

Taranto, Damian Eduardo; Bormetti, Giacomo; Bouchaud, Jean‐Philippe; Lillo, Fabrizio; Tóth, Bence

doi:10.1080/14697688.2017.1395903

Cited by 22 publications

(12 citation statements)

References 25 publications

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“…A linear relationship between participation rate and interval return ( Figure 5) is observed. In real trading, however, there is evidence that this relationship is better described by the declining marginal effect of participation rate on market impact and concave curve (Toth et al, 2011;Bershova & Rakhlin, 2013;Taranto et al, 2018). This discrepancy highlights an important limitation of the model: it does not take into account that market participants adjust their trading based on the current market activity.…”

Section: Speed Of Trading Effect On the Modelmentioning

confidence: 99%

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Metaorder limit prices in evaluating expected market impact and assessing execution service quality

Bazylevych

Ihnatiuk

2019

Investment Management and Financial Innovations

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The paper examines the bias introduced by metaorder limit prices when measuring quality of execution services on financial market. While evaluating the quality of execution services, observed execution costs should be adjusted for metaorder participation rate, size and duration to ensure that they are comparable across execution service providers. One of the exogenous factors which may bias measured execution costs are the different metaorder limit prices in the sample. Currently, there are no proposed methods to normalize for this bias. In the research, the difference in execution costs for metaorders with different limit prices was examined by implementing a limit order book simulation model. It was discovered that the difference in metaorder limit prices is a source of significant heterogeneity in the execution cost distribution. However, we were able to prove that when market agents trade with constant intensities, the difference in execution costs for metaorders with different limit prices is fully explained by their realized participation rate. As a result, financial institution may assess quality of execution services for metaorders without any reservations about differences in metaorders limit prices as long as execution costs are adjusted for different participation rates.

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Section: Speed Of Trading Effect On the Modelmentioning

confidence: 99%

“…Additionally, we investigated the interval return (which is also known as 'market impact' in Bershova Arrival price (21) where Departure price -midpoint of price at the end of executing client order.…”

Section: Speed Of Trading Effect On the Modelmentioning

confidence: 99%

Metaorder limit prices in evaluating expected market impact and assessing execution service quality

Bazylevych

Ihnatiuk

2019

Investment Management and Financial Innovations

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“…A series of previous studies mainly focus on the impact of imbalances between buy and sell market orders. Taranto et al [47] propose a framework aiming to build a precise linear model of price dynamics based on two types of market orders: (1) those that leave the price unchanged, and (2) those which lead to an immediate price change. Brown et al [9] adopt a bivariate vector auto-regression (VAR) to study the interaction between order imbalance and stock return.…”

Section: Related Literaturementioning

confidence: 99%

Price Impact of Order Flow Imbalance: Multi-level, Cross-sectional and Forecasting

2021

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“…There has been a debate concerning the contradictory facts between the predictable order flow and unpredictable price due to price impact [16,22]. Recently, Taranto et al [23,24] showed that the history-dependent impact model is superior to the transient impact model. Understanding the extreme and large event on the order book is also an active research field because an event greatly influences a market.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of the aggressive order during a crisis

Lee

Jung

2020

PLoS ONE

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We investigate the dynamics of aggressive order in the financial market to further understand volatility. To analyze aggressive order, market orders in the order book are scrutinized. The market orders have different degrees of aggressiveness; therefore, we categorize market orders into four types: types Zero, One, A, and B, of which type B is the most aggressive. To examine the dynamics and impacts of each type of order, we use both macro-and micro-level approaches. From the macroscopic perspective, the burstiness and memory of type B is highly correlated with volatility. When traders face a financial crisis, they place bursty aggressive orders, and the orders are more predictable than usual. From the microscopic perspective, we additionally focus on the influence of the orders, particularly the price impact and resilience. The aggressive order has a greater impact than others, even when the price change of the aggressive order is smaller. Moreover, the aggressive order delivers more information on price because the aggressive order has a higher price impact than the execution cost.

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Linear models for the impact of order flow on prices. I. History dependent impact models

Cited by 22 publications

References 25 publications

Metaorder limit prices in evaluating expected market impact and assessing execution service quality

Metaorder limit prices in evaluating expected market impact and assessing execution service quality

Price Impact of Order Flow Imbalance: Multi-level, Cross-sectional and Forecasting

The dynamics of the aggressive order during a crisis

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