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Optimal Arbitrage Trading

Author

Listed:
  • Michael Boguslavsky

    (ABN-AMRO Global Equity Derivatives)

  • Elena Boguslavskaya

    (University of Amsterdam)

Abstract

We consider the position management problem for an agent trading a mean- reverting asset. This problem arises in many statistical and fundamental arbitrage trading situations when the short-term returns on an asset are predictable but limited risk-bearing capacity does not allow to fully exploit this predictability. The model is rather simple; it does not require any inputs apart from the parameters of the price process and agent's relative risk aversion. However, the model reproduces some realistic patterns of traders' behaviour. We use the Ornstein-Uhlenbeck process to model the price process and consider a finite horizon power utility agent. The dynamic programming approach yields a non-linear PDE. It is solved explicitly, and simple formulas for the value function and the optimal trading strategy are obtained. We use Monte-Carlo simulation to check for the effects of parameter misspecification.

Suggested Citation

  • Michael Boguslavsky & Elena Boguslavskaya, 2003. "Optimal Arbitrage Trading," Finance 0309012, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0309012
    Note: Type of Document - pdf; prepared on IBM PC LaTeX; pages: 13; figures: included
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0309/0309012.pdf
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    References listed on IDEAS

    as
    1. Lakner, Peter, 1998. "Optimal trading strategy for an investor: the case of partial information," Stochastic Processes and their Applications, Elsevier, vol. 76(1), pages 77-97, August.
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    More about this item

    Keywords

    arbitrage trading; mean-reverting process; stochastic optimal control;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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