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Measuring Portfolio Value at Risk

Xu, Chao LU and Chen, Huigeng LU (2012) NEKN02 20121
Department 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)
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author
Xu, Chao LU and Chen, Huigeng LU
supervisor
organization
course
NEKN02 20121
year
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},
  keyword      = {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     = {eng},
  note         = {Student Paper},
  title        = {Measuring Portfolio Value at Risk},
  year         = {2012},
}