Estimating Risk Using Stochastic Volatility Models and Particle Stochastic Approximation Expectation Maximization
(2020) In Master's Theses in Mathematical Sciences MASM01 20201Mathematical 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:
http://lup.lub.lu.se/student-papers/record/9025323
- author
- Kragh, Henrik LU
- supervisor
- organization
- course
- MASM01 20201
- year
- 2020
- 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}}, }