Modeling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance

Brière, Marie; Lehalle, Charles Albert; Nefedova, Tamara; Raboun, Amine

doi:10.1002/9781119751182.ch11

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…6 See Figure 1 in Tóth et al (2011). 7 For instance, the square-root model is used by Gârleanu and Pedersen (2013), Frazzini et al (2018) and Briere et al (2020).…”

Section: 3mentioning

confidence: 99%

Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk

Roncalli¹,

Cherief²,

Karray-Meziou³

et al. 2021

Preprint

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This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019(ESMA, , 2020 and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers.In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.

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“…6 See Figure 1 in Tóth et al (2011). 7 For instance, the square-root model is used by Gârleanu and Pedersen (2013), Frazzini et al (2018) and Briere et al (2020).…”

Section: 3mentioning

confidence: 99%

Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk

Roncalli¹,

Cherief²,

Karray-Meziou³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…From a practical perspective, the model and the empirical observations are important for traders to estimate (pre-and post-execution) the cost of their trades, and thus to help them deciding when is the right moment to trade. For example, (Briere et al, 2020), investigating the ANcerno database, finds an approximately linear relation between the implementation shortfall of a metaorder and the net trading imbalance due to the other metaorders simultaneously traded. When the trade is in the same direction as the net order flow imbalance, one could expect to pay a significant trading cost up to 0.4 points of price volatility, while one could expect to benefit from a price improvement of 0.3 points of volatility when the trader is almost alone in front of his competitors aggregate flow.…”

Section: Co-impactmentioning

confidence: 99%

Order flow and price formation

Lillo¹

2021

Preprint

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I present an overview of some recent advancements on the empirical analysis and theoretical modeling of the process of price formation in financial markets as the result of the arrival of orders in a limit order book exchange. After discussing critically the possible modeling approaches and the observed stylized facts of order flow, I consider in detail market impact and transaction cost of trades executed incrementally over an extended period of time, by comparing model predictions and recent extensive empirical results. I also discuss how the simultaneous presence of many algorithmic trading executions affects the quality and cost of trading.

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Cumulative market impact of consecutive orders over one and two days: how long does the market remember past trades?

Besson¹,

Lasnier²

2021

Quantitative Finance

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Modeling Transaction Costs When Trades May Be Crowded: A Bayesian Network Using Partially Observable Orders Imbalance

Cited by 7 publications

References 30 publications

Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk

Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk

Order flow and price formation

Cumulative market impact of consecutive orders over one and two days: how long does the market remember past trades?

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