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Estimating Risk Using Stochastic Volatility Models and Particle Stochastic Approximation Expectation Maximization

Kragh, Henrik LU (2020) In Master's Theses in Mathematical Sciences MASM01 20201
Mathematical Statistics
Abstract
In this thesis several stochastic volatility models are presented and used to estimate the risk of a collection of Swedish stocks, as well as of a portfolio consisting of said stocks. Model parameters are estimated using the PSAEM algorithm. It is concluded that these model are adequate at estimating the one day ahead five percent Value at Risk of the data in terms of conditional coverage.
Please use this url to cite or link to this publication:
author
Kragh, Henrik LU
supervisor
organization
course
MASM01 20201
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUNFMS-3095-2020
ISSN
1404-6342
other publication id
2020:E62
language
English
id
9025323
date added to LUP
2020-09-24 09:41:25
date last changed
2021-06-04 18:32:21
@misc{9025323,
  abstract     = {{In this thesis several stochastic volatility models are presented and used to estimate the risk of a collection of Swedish stocks, as well as of a portfolio consisting of said stocks. Model parameters are estimated using the PSAEM algorithm. It is concluded that these model are adequate at estimating the one day ahead five percent Value at Risk of the data in terms of conditional coverage.}},
  author       = {{Kragh, Henrik}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Estimating Risk Using Stochastic Volatility Models and Particle Stochastic Approximation Expectation Maximization}},
  year         = {{2020}},
}