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Permanent market impact can be nonlinear

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  • Olivier Gu'eant

Abstract

There are two schools of thought regarding market impact modeling. On the one hand, seminal papers by Almgren and Chriss introduced a decomposition between a permanent market impact and a temporary (or instantaneous) market impact. This decomposition is used by most practitioners in execution models. On the other hand, recent research advocates for the use of a new modeling framework that goes down to the resilient dynamics of order books: transient market impact. One of the main criticisms against permanent market impact is that it has to be linear to avoid dynamic arbitrage. This important discovery made by Huberman and Stanzl and Gatheral favors the transient market impact framework, as linear permanent market impact is at odds with reality. In this paper, we reconsider the point made by Gatheral using a simple model for market impact and show that permanent market impact can be nonlinear. Also, and this is the most important part from a practical point of view, we propose different statistics to estimate permanent market impact and execution costs that generalize the ones proposed in Almgren at al. (2005).

Suggested Citation

  • Olivier Gu'eant, 2013. "Permanent market impact can be nonlinear," Papers 1305.0413, arXiv.org, revised Mar 2014.
  • Handle: RePEc:arx:papers:1305.0413
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    References listed on IDEAS

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    1. Aurélien Alfonsi & Alexander Schied, 2010. "Optimal trade execution and absence of price manipulations in limit order book models," Post-Print hal-00397652, HAL.
    2. Gur Huberman & Werner Stanzl, 2004. "Price Manipulation and Quasi-Arbitrage," Econometrica, Econometric Society, vol. 72(4), pages 1247-1275, July.
    3. Aur'elien Alfonsi & Antje Fruth & Alexander Schied, 2007. "Optimal execution strategies in limit order books with general shape functions," Papers 0708.1756, arXiv.org, revised Feb 2010.
    4. Aurelien Alfonsi & Antje Fruth & Alexander Schied, 2010. "Optimal execution strategies in limit order books with general shape functions," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 143-157.
    5. Obizhaeva, Anna A. & Wang, Jiang, 2013. "Optimal trading strategy and supply/demand dynamics," Journal of Financial Markets, Elsevier, vol. 16(1), pages 1-32.
    6. Alexander Schied & Torsten Schöneborn, 2009. "Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets," Finance and Stochastics, Springer, vol. 13(2), pages 181-204, April.
    7. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
    8. Alexander Schied & Torsten Schoneborn & Michael Tehranchi, 2010. "Optimal Basket Liquidation for CARA Investors is Deterministic," Applied Mathematical Finance, Taylor & Francis Journals, vol. 17(6), pages 471-489.
    9. Julian Lorenz & Robert Almgren, 2011. "Mean--Variance Optimal Adaptive Execution," Applied Mathematical Finance, Taylor & Francis Journals, vol. 18(5), pages 395-422, January.
    10. Robert Almgren, 2003. "Optimal execution with nonlinear impact functions and trading-enhanced risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(1), pages 1-18.
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