Multivariate Risk: From Univariate to High-Dimensional Graphical Models
(2020) STAN40 20191Department of Statistics
- Abstract
- We present a comparison of different univariate and multivariate extreme value risk models. Our focus is on exploring how these can be used to model financial risk. We use simulated as well as real data and compare deterministic and cross-validation threshold selection methods for the GP model to a GEV model. For comparison, we carry out a bivariate analysis using copulas. Finally, an undirected graphical lasso model using n=45 block maxima of the log-returns from 95 of the stocks in the FTSE 100 index is combined with copulas and PCA to model the extreme loss risk within the FTSE 100 index. The contribution of this study lies in exploring some ideas on risk models in multivariate high-dimensional settings.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8996517
- author
- Oldehed, Erik
- supervisor
-
- Sreekar Vadlamani LU
- Marie Kratz LU
- organization
- course
- STAN40 20191
- year
- 2020
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Block Maxima, Mean Excess Plot, Tail Risk, Cross-Validation Threshold Selection, Graphical Lasso, Nonparanormal Distribution.
- language
- English
- id
- 8996517
- date added to LUP
- 2020-06-22 11:06:17
- date last changed
- 2020-06-22 11:06:17
@misc{8996517, abstract = {{We present a comparison of different univariate and multivariate extreme value risk models. Our focus is on exploring how these can be used to model financial risk. We use simulated as well as real data and compare deterministic and cross-validation threshold selection methods for the GP model to a GEV model. For comparison, we carry out a bivariate analysis using copulas. Finally, an undirected graphical lasso model using n=45 block maxima of the log-returns from 95 of the stocks in the FTSE 100 index is combined with copulas and PCA to model the extreme loss risk within the FTSE 100 index. The contribution of this study lies in exploring some ideas on risk models in multivariate high-dimensional settings.}}, author = {{Oldehed, Erik}}, language = {{eng}}, note = {{Student Paper}}, title = {{Multivariate Risk: From Univariate to High-Dimensional Graphical Models}}, year = {{2020}}, }