Andersen–Broadie Dual Bounds and Exercise-Strategy Estimation
Summary
The document explains how the Andersen–Broadie primal-dual approach constructs an upper bound for an American option’s value from an exercise strategy. The strategy is not obtained by knowing the exact option value in advance: a continuation-value approximation, often produced by regression, can guide exercise decisions. The answer distinguishes this strategy-building step from the later calculation used to estimate the dual bound.
For the bound calculation, nested Monte Carlo simulations estimate conditional values along simulated paths. The response says these sub-simulations produce noisy estimates with upward bias, a feature used in the Andersen–Broadie argument to preserve an upper bound. Replacing that step with regression does not generally give the same unbiased estimate or guarantee the bound. The document therefore offers a conceptual resolution to the apparent circularity, while noting that sub-simulation noise and cost have motivated methods to reduce or avoid them. It does not provide implementation details, numerical comparisons, or a full derivation of the estimator.
Key ideas
- The Andersen–Broadie dual method turns an exercise strategy into an upper bound for an American option.
- A continuation-value approximation can be used to construct the exercise strategy.
- Nested simulations estimate conditional values needed to evaluate the strategy along paths.
- Regression at the conditional-value estimation stage does not automatically preserve the upper-bound property.
- Sub-simulation noise contributes upward bias, and the document points to research on reducing that effect.
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Full text
# Andersen Broadie American/Bermudan Put # Andersen Broadie American/Bermudan Put I'm trying to implement Andersen and Broadie's dual method for an upper bound (here) of a regular American Put. I understand the process to compute it, but I have a conceptual issue : everything relies on the computation of an approximation of the Doob's martingale. This approximation can be computed through Monte-carlo sub-simulations using a stopping rule to determine where to start and stop sub paths. So far, so good. But when looking for the stopping rule for the American Put (there, section 2.3.1), I see that optimal exercises times are defined as the instants when (K-S)+ is above the value of the Put... which seems like going around in circles to me (to compute the put you need the exercise times, to get the exercise times you need the put). So how to concretely proceed ? (Others use regression to compute the conditional expectations, but since Andersen and Broadie don't, I was wondering whether we could avoid it in this case) ## Answer by Mark Joshi (score 1, accepted) https://quant.stackexchange.com/a/25818 Andersen--Broadie converts an exercise strategy into an upper bound. The better the exercise strategy the better the upper bound. You can get the exercise strategy by using regression to approximate the continuation value and this is pretty standard -- the LS Method is widely used but does have defects. Once you have an exercise strategy you need the value of the product using that strategy at each exercise time on each path. The only effective way to get this is a sub-simulation which gives a noisy estimate of an unbiased estimate. Regression at this point doesn't give an unbiased estimate so you are not guaranteed an upper bound. Andersen and Broadie prove that the noise in the sub-sims lead to additional upwards bias. There has been considerable work on the problem of avoiding sub-sims and on reducing the effect of their noise. See, for example, my two papers Effective Sub-Simulation-Free Upper Bounds for the Monte Carlo Pricing of Callable Derivatives and Various Improvements to Existing Methodologies Joshi and Tang Analyzing the Bias in the Primal-Dual Upper Bound Method for Early Exercisable Derivatives: Bounds, Estimation and Removal Mark S. Joshi
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