Scenario generation by selection from historical data

Michal Kaut ()
Additional contact information
Michal Kaut: SINTEF

Computational Management Science, 2021, vol. 18, issue 3, No 7, 429 pages

Abstract: Abstract In this paper, we present and compare several methods for generating scenarios for stochastic-programming models by direct selection from historical data. The methods range from standard sampling and k-means, through iterative sampling-based selection methods, to a new moment-based optimization approach. We compare the models on a simple portfolio-optimization model and show how to use them in a situation when we are selecting whole sequences from the data, instead of single data points.

Keywords: Stochastic programming; Scenario generation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10287-021-00399-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:18:y:2021:i:3:d:10.1007_s10287-021-00399-4

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

DOI: 10.1007/s10287-021-00399-4

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 ().