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Accelerated Share Repurchase: Pricing And Execution Strategy

Author

Listed:
  • OLIVIER GUÉANT

    (Université Paris-Diderot, UFR de Mathématiques, Laboratoire Jacques-Louis Lions, France)

  • JIANG PU

    (Institut Europlace de Finance, Research Initiative "Exécution optimale et statistiques, de la liquidité haute fréquence", France)

  • GUILLAUME ROYER

    (CMAP, Ecole Polytechnique, Paris, France)

Abstract

In this paper, we consider the optimal execution problem associated to accelerated share repurchase (ASR) contracts. When firms want to repurchase their own shares, they often enter such a contract with a bank. The bank buys the shares for the firm and is paid the average market price over the execution period, the length of the period being decided upon by the bank during the buying process. Mathematically, the problem is new and related to both option pricing (Asian and Bermudan options) and optimal execution. We provide a model, along with associated numerical methods, to determine the optimal stopping time and the optimal buying strategy of the bank.

Suggested Citation

  • Olivier Guéant & Jiang Pu & Guillaume Royer, 2015. "Accelerated Share Repurchase: Pricing And Execution Strategy," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1-31.
    • Handle: RePEc:wsi:ijtafx:v:18:y:2015:i:03:n:s0219024915500193
    DOI: 10.1142/S0219024915500193
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    References listed on IDEAS

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    1. Charles-Albert Lehalle & Sophie Laruelle (ed.), 2013. "Market Microstructure in Practice," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8967, December.
    2. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Citations

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    Cited by:

    1. Mohamed Hamdouche & Pierre Henry-Labordere & Huyen Pham, 2023. "Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing," Papers 2302.07320, arXiv.org.
    2. Olivier Guéant & Iuliia Manziuk & Jiang Pu, 2020. "Accelerated Share Repurchase and other buyback programs: what neural networks can bring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02987889, HAL.
    3. David Evangelista & Yuri Saporito & Yuri Thamsten, 2022. "Price formation in financial markets: a game-theoretic perspective," Papers 2202.11416, arXiv.org.
    4. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    5. Olivier Guéant & Iuliia Manziuk & Jiang Pu, 2020. "Accelerated Share Repurchase and other buyback programs: what neural networks can bring," Working Papers hal-02987889, HAL.
    6. Alexis Bismuth & Olivier Gu'eant & Jiang Pu, 2016. "Portfolio choice, portfolio liquidation, and portfolio transition under drift uncertainty," Papers 1611.07843, arXiv.org, revised Mar 2019.
    7. Olivier Gu'eant & Iuliia Manziuk & Jiang Pu, 2019. "Accelerated Share Repurchase and other buyback programs: what neural networks can bring," Papers 1907.09753, arXiv.org, revised Nov 2019.
    8. Olivier Guéant & Jiang Pu, 2015. "Option pricing and hedging with execution costs and market impact," Post-Print hal-01393124, HAL.

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