Generation of Synthetic Data

Dany Cajas
Additional contact information
Dany Cajas: Orenji EIRL

Chapter Chapter 14 in Advanced Portfolio Optimization, 2025, pp 399-435 from Springer

Abstract: Abstract This chapter explains three approaches that allow readers to generate synthetic data to backtest our multiassets investment strategies: block bootstrap, copulas, and econometric models. These techniques will allow readers to create several types of scenarios that will help them to backtest their strategies during different market conditions. This chapter does not delve much into the theory; it mainly focuses on showing how to use each approach to generate synthetic data using Python code.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-3-031-84304-4_14

Ordering information: This item can be ordered from
http://www.springer.com/9783031843044

DOI: 10.1007/978-3-031-84304-4_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().