Process-Based Risk Measures and Risk-Averse Control of Discrete-Time Systems
Jingnan Fan and
Andrzej Ruszczynski (rusz@business.rutgers.edu)
Papers from arXiv.org
Abstract:
For controlled discrete-time stochastic processes we introduce a new class of dynamic risk measures, which we call process-based. Their main features are that they measure risk of processes that are functions of the history of a base process. We introduce a new concept of conditional stochastic time consistency and we derive the structure of process-based risk measures enjoying this property. We show that they can be equivalently represented by a collection of static law-invariant risk measures on the space of functions of the state of the base process. We apply this result to controlled Markov processes and we derive dynamic programming equations.
Date: 2014-11, Revised 2016-11
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1411.2675
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