Pathwise Dynamic Programming

Christian Bender (), Christian Gärtner () and Nikolaus Schweizer ()
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Christian Bender: Department of Mathematics, Saarland University, Postfach 151150, D-66041 Saarbrücken, Germany
Christian Gärtner: Department of Mathematics, Saarland University, Postfach 151150, D-66041 Saarbrücken, Germany
Nikolaus Schweizer: Department of Econometrics and Operations Research, Tilburg University, NL-5000 LE Tilburg, Netherlands

Mathematics of Operations Research, 2018, vol. 43, issue 3, 965-965

Abstract: We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and upper bounds ensures that the method is applicable even when the dynamic program does not satisfy a comparison principle. We apply our method to three nonlinear option pricing problems, pricing under bilateral counterparty risk, under uncertain volatility, and under negotiated collateralization.

Keywords: stochastic dynamic programming; Monte Carlo; confidence bounds; option pricing (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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