Multistage Robust Average Randomized Spectral Risk Optimization

Abstract

In this paper, we revisit the multistage spectral risk minimization models proposed by Philpott et al.~\cite{PdF13} and Guigues and R\"omisch \cite{GuR12} but with some new focuses. We consider a situation where the decision maker's (DM's) risk preferences may be state-dependent or even inconsistent at some states, and consequently there is not a single deterministic spectral risk measure (SRM) which can be used to represent the DM's preferences at each stage. We adopt the recently introduced average randomized SRM (ARSRM) (in \cite{li2022randomization}) to describe the DM's overall risk preference at each stage. To solve the resulting multistage ARSRM (MARSRM) problem, we apply the well-known stochastic dual dynamic programming (SDDP) method which generates a sequence of lower and upper bounds in an iterative manner. Under some moderate conditions, we prove that the optimal solution can be found in a finite number of iterations. The MARSRM model generalizes the one-stage ARSRM and simplifies the existing multistage state-dependent preference robust model \cite{liu2021multistage}, while also encompassing the mainstream multistage risk-neutral and risk-averse optimization models \cite{GuR12,PdF13}. In the absence of complete information on the probability distribution of the DM's random preferences, we propose to use distributionally robust ARSRM (DR-ARSRM) to describe the DM's preferences at each stage. We detail computational schemes for solving both MARSRM and DR-MARSRM. Finally, we examine the performance of MARSRM and DR-MARSRM by applying them to an asset allocation problem with transaction costs and compare them with standard risk neutral and risk averse multistage linear stochastic programming (MLSP) models.