Nonlinear predictable representation and L1-solutions of backward SDEs and second-order backward SDEs

Zhenjie Ren, Nizar Touzi, Junjian Yang

Research output: Contribution to journalArticlepeer-review

Abstract

The theory of backward SDEs extends the predictable representation property of Brownian motion to the nonlinear framework, thus providing a path-dependent analog of fully nonlinear parabolic PDEs. In this paper, we consider backward SDEs, their reflected version, and their second-order extension, in the context where the final data and the generator satisfy L1-integrability condition. Our main objective is to provide the corresponding existence and uniqueness results for general Lipschitz generators. The uniqueness holds in the so-called Doob class of processes, simultaneously under an appropriate class of measures. We emphasize that the previous literature only deals with backward SDEs, and requires either that the generator is separable in (y, z), see Peng (In Backward Stochastic Differential Equations (1997) 141-159 Longman), or strictly sublinear in the gradient variable z, see Briand, Delyon, Hu, Pardoux and Stoica (Stochastic Process. Appl. 108 (1) (2003) 109-129), or that the final data satisfies an LlnL-integrability condition, see Hu and Tang (Electron. Commun. Probab. 23 (2018) 27).We bypass these conditions by defining L1-integrability under the nonlinear expectation operator induced by the previously mentioned class of measures.

Original languageEnglish (US)
Pages (from-to)639-666
Number of pages28
JournalAnnales de l'institut Henri Poincare (B) Probability and Statistics
Volume58
Issue number2
DOIs
StatePublished - May 2022

Keywords

  • Backward SDE
  • Nondominated probability measures
  • Nonlinear expectation
  • Second-order backward SDE

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Nonlinear predictable representation and L1-solutions of backward SDEs and second-order backward SDEs'. Together they form a unique fingerprint.

Cite this