On variance reduction of mean-CVaR Monte Carlo estimators

Václav Kozmík ()

Computational Management Science, 2015, vol. 12, issue 2, 242 pages

Abstract: We formulate an objective as a convex combination of expectation and risk, measured by the $$\mathrm{CVaR }$$ CVaR risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance sampling is proposed. We provide analytical solution for random variables based on normal distribution and outline the way for the other distributions, either by analytical computation or by sampling. Our results are applied in the framework of stochastic dual dynamic programming algorithm. Computational results which validate the previous analysis are given. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Importance sampling; Risk-averse optimization; Monte Carlo sampling; Stochastic dual dynamic programming; 65C05; 90C15; 91G60 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s10287-014-0225-7 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:12:y:2015:i:2:p:221-242

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-014-0225-7

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().