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Static Versus Adapted Optimal Execution Strategies in Two Benchmark Trading Models

In: Innovations in Insurance, Risk- and Asset Management

Author

Listed:
  • Damiano Brigo
  • Clément Piat

Abstract

We consider the optimal solutions to the trade execution problem in the two different classes of i) fully adapted or adaptive and ii) deterministic or static strategies, comparing them. We do this in two different benchmark models. The first model is a discrete time framework with an information flow process, dealing with both permanent and temporary impact, minimizing the expected cost of the trade. The second model is a continuous time framework where the objective function is the sum of the expected cost and a value at risk (or expected shortfall) type risk criterion. Optimal adapted solutions are known in both frameworks from the original works of Bertsimas and Lo (1998) and Gatheral and Schied (2011). In this paper we derive the optimal static strategies for both benchmark models and we study quantitatively the improvement in optimality when moving from static strategies to fully adapted ones. We conclude that, in the benchmark models we study, the difference is not relevant, except for extreme unrealistic cases for the model or impact parameters.

Suggested Citation

  • Damiano Brigo & Clément Piat, 2018. "Static Versus Adapted Optimal Execution Strategies in Two Benchmark Trading Models," World Scientific Book Chapters, in: Kathrin Glau & Daniël Linders & Aleksey Min & Matthias Scherer & Lorenz Schneider & Rudi Zagst (ed.), Innovations in Insurance, Risk- and Asset Management, chapter 10, pages 239-273, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789813272569_0010
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    Citations

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    Cited by:

    1. Claudio Bellani & Damiano Brigo & Alex Done & Eyal Neuman, 2018. "Static vs Adaptive Strategies for Optimal Execution with Signals," Papers 1811.11265, arXiv.org, revised Jul 2019.
    2. Claudio Bellani & Damiano Brigo, 2021. "Mechanics of good trade execution in the framework of linear temporary market impact," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 143-163, January.
    3. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.

    More about this item

    Keywords

    Insurance; Actuarial Science; Risk Measure; Reinsurance; Copula; Replicating Portfolio; Bayesian Finance; Risk Classification; Stochastic Dominance; Dynamic Hedging; Autoregressive Hidden Markov Models; Exchange-Traded Funds; Uncertainty Quantification; Fixed Income; Stochastic Processes for Finance;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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