Monte Simulation de Monte Carlo Carlo Simulations

Patrice Poncet () and Roland Portait
Additional contact information
Patrice Poncet: ESSEC Business School
Roland Portait: ESSEC Business School

Chapter 26 in Capital Market Finance, 2022, pp 1063-1102 from Springer

Abstract: Abstract Monte Carlo simulation is a probabilistic technique used when the probability distribution of the investor’s portfolio value cannot be represented by a closed formula and its empirical distribution hence needs to be simulated. The Monte Carlo methodology relies upon the generation of a large number of random draws from a given probability distribution. We describe in Sect. 26.1 the methodologies to be used to perform such random samplings. We then explain in Sect. 26.2 how to perform Monte Carlo simulation with a single risk factor, before addressing in Sect. 26.3 the simulation with multiple risk factors. Section 26.4 presents some observations regarding the efficiency of the simulation framework and introduces some techniques to improve this efficiency. Section 26.5 proposes a solution to the problem posed by American option pricing.

Date: 2022
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:sptchp:978-3-030-84600-8_26

Ordering information: This item can be ordered from
http://www.springer.com/9783030846008

DOI: 10.1007/978-3-030-84600-8_26

Access Statistics for this chapter

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