Robust optimal stopping
- Krätschmer, Volker
- Ladkau, Marcel
- Laeven, Roger J. A.
- Schoenmakers, John G. M.
- Stadje, Mitja
2010 Mathematics Subject Classification
- 49L20 60G40 91B06
- Optimal stopping, model uncertainty, robustness, convex risk measures, ambiguity aversion, duality, BSDEs, Monte Carlo simulation, regression, relative entropy
This paper studies the optimal stopping problem in the presence of model uncertainty (ambiguity). We develop a method to practically solve this problem in a general setting, allowing for general time-consistent ambiguity averse preferences and general payoff processes driven by jump-diffusions. Our method consists of three steps. First, we construct a suitable Doob martingale associated with the solution to the optimal stopping problem represented by the Snell envelope using backward stochastic calculus. Second, we employ this martingale to construct an approximated upper bound to the solution using duality. Third, we introduce backward-forward simulation to obtain a genuine upper bound to the solution, which converges to the true solution asymptotically. We analyze the asymptotic behavior and convergence properties of our method. We illustrate the generality and applicability of our method and the potentially significant impact of ambiguity to optimal stopping in a few examples.
- Math. Oper. Res., (2018), published online on 09.08.2018, DOI 10.1287/moor.2017.0899, under the new title: Optimal stopping under uncertainty in drift and jump intensity.