Monte Carlo methods for the estimation of value-at-risk and related risk measures

Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss. The algorithms are designed to take advantage of multiprocessing computer architecture by performing computational tasks in parallel. Through numerical experiments we show that our algorithms can improve efficiency in the sense of reducing mean-squared error.

Grantor dc:publisher.institution

Division of Actuarial Science

Year dc:date.issued

2011

Author dc:creator

• Marks, Dean

Advisor dc:contributor.advisor

• Becker, Ronald

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/10966

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/10966