A primal-dual algorithm for BSDEs

Christian Bender, Nikolaus Schweizer, Jia Zhuo

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)


We generalize the primal–dual methodology, which is popular in the pricing of early-exercise options, to a backward dynamic programming equation associated with time discretization schemes of (reflected) backward stochastic differential equations (BSDEs). Taking as an input some approximate solution of the backward dynamic program, which was precomputed, e.g., by least-squares Monte Carlo, this methodology enables us to construct a confidence interval for the unknown true solution of the time-discretized (reflected) BSDE at time 0. We numerically demonstrate the practical applicability of our method in two 5-dimensional nonlinear pricing problems where tight price bounds were previously unavailable.
Original languageEnglish
Pages (from-to)866-901
JournalMathematical Finance
Issue number3
Publication statusPublished - 1 Jul 2017


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