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Ambiguous Jump-Diffusions and Optimal Stopping

Author

Listed:
  • Svetlana Boyarchenko

    () (Department of Economics, University of Texas at Austin)

  • Sergei Levendorskii

    (Department of Mathematics, University of Leicester)

Abstract

An ambiguity averse decision-maker contemplates investment of a fixed size capital into a project with a stochastic profit stream under the Knightian uncertainty. Multiple priors are modeled as a ``cloud" of diffusion processes with embedded compound Poisson jumps. The ``cloud" contains the Brownian motion (BM) as a process with zero density of jumps. The decision-maker has recursive multiple priors utility as in Epstein and Schneider (2003) and chooses the optimal investment timing. We demonstrate that if the expected present value (EPV) of the project is the same for each jump-diffusion prior at the moment of investment, then the BM is the worst prior in the waiting region. The same conclusion holds for some parameter values even when the BM gives the highest EPV of the project. For other parameter values, it is possible that the local dynamics of the worst case prior is given by a jump-diffusion in a vicinity of the investment threshold and by the BM in a vicinity of negative infinity. Explicit formulas for the value functions and investment thresholds are derived.

Suggested Citation

  • Svetlana Boyarchenko & Sergei Levendorskii, 2014. "Ambiguous Jump-Diffusions and Optimal Stopping," Department of Economics Working Papers 141031, The University of Texas at Austin, Department of Economics.
  • Handle: RePEc:tex:wpaper:141031
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    File URL: http://ssrn.com/abstract=2514032orhttp://dx.doi.org/10.2139/ssrn.2514032
    File Function: First version, 2014
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    More about this item

    Keywords

    optimal stopping; jump-diffusion process; ambiguity;

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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