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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 multipro...
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| Format: | Thesis |
| Language: | English |
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Division of Actuarial Science
2015
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