Measuring Portfolio Value at Risk
(2012) NEKN02 20121Department of Economics
- Abstract
- On estimating portfolio Value at Risk, the application of traditional univariate VaR models is limited. Under specific circumstance, the VaR estimation could be inadequate. Facing the financial crises and increasing uncertainty in financial markets, effective multivariate VaR models have become crucial. This paper gives an overview of various multivariate VaR models. The main aim is to compare the one day out-of-sample predictive performances of different models, including basic multivariate VaR models, volatility weighted multivariate VaR models and copula-based multivariate VaR models. Performance is evaluated in terms of Christoffersen test, quadratic probability score and root mean squared error. The findings show that basic... (More)
- On estimating portfolio Value at Risk, the application of traditional univariate VaR models is limited. Under specific circumstance, the VaR estimation could be inadequate. Facing the financial crises and increasing uncertainty in financial markets, effective multivariate VaR models have become crucial. This paper gives an overview of various multivariate VaR models. The main aim is to compare the one day out-of-sample predictive performances of different models, including basic multivariate VaR models, volatility weighted multivariate VaR models and copula-based multivariate VaR models. Performance is evaluated in terms of Christoffersen test, quadratic probability score and root mean squared error. The findings show that basic multivariate VaR models such as multivariate normal VaR model and multivariate t VaR model behave poorly and fail to generate reliable VaR estimations. By contrast, volatility weighted multivariate VaR models and copula-based multivariate VaR models show notable improvements in the predictive performance. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/2628584
- author
- Xu, Chao LU and Chen, Huigeng LU
- supervisor
- organization
- course
- NEKN02 20121
- year
- 2012
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Multivariate Value at Risk, portfolio risk measures, Copula, Monte Carlo simulation, DCC-GARCH, multivariate EWMA, Christoffersen test, quadratic probability score, root mean squared error, R software.
- language
- English
- id
- 2628584
- date added to LUP
- 2012-06-08 14:30:16
- date last changed
- 2012-06-08 14:30:16
@misc{2628584, abstract = {{On estimating portfolio Value at Risk, the application of traditional univariate VaR models is limited. Under specific circumstance, the VaR estimation could be inadequate. Facing the financial crises and increasing uncertainty in financial markets, effective multivariate VaR models have become crucial. This paper gives an overview of various multivariate VaR models. The main aim is to compare the one day out-of-sample predictive performances of different models, including basic multivariate VaR models, volatility weighted multivariate VaR models and copula-based multivariate VaR models. Performance is evaluated in terms of Christoffersen test, quadratic probability score and root mean squared error. The findings show that basic multivariate VaR models such as multivariate normal VaR model and multivariate t VaR model behave poorly and fail to generate reliable VaR estimations. By contrast, volatility weighted multivariate VaR models and copula-based multivariate VaR models show notable improvements in the predictive performance.}}, author = {{Xu, Chao and Chen, Huigeng}}, language = {{eng}}, note = {{Student Paper}}, title = {{Measuring Portfolio Value at Risk}}, year = {{2012}}, }