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Comparing Value-at-Risk Methodologies

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Author Info
Luiz Renato Lima
Breno Pinheiro Néri () (Graduate School of Economics Getúlio Vargas Foundation)

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Abstract

We perform a Monte Carlo experimet to compare four different Value-at-Risk methodologies, RiskMetrics, Gaussian GARCH(1,1), Generalized Student-t APARCH(1,1), and ARCH(1) Quantile, under five different data generating processes. The ARCH(1) Quantile methodology does not assume any distribution for the returns, and this robustness is shown to avoid trajectories with too many violations. The number of violations tends to be higher in the non-robust methodologies when the distribution differs from the Gaussian one. We also perform an empirical exercise applying the four Value-at-Risk methodologies to daily return of the IBOVESPA (measured in dollar values) in a period of market turmoil (1996-2000), when happens the Korean crisis, the Russian crisis and the blast of the technology-stock market bubble. We display that, again, the ARCH(1) Quantile methodology dominates the non-robust methodologies, in the sense that it presents the least number of violations

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 1.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:1

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Related research
Keywords: ARCH Quantile Value-at-Risk

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Find related papers by JEL classification:
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Zhijie Xiao & Luiz Renato Regis de Oliveira Lima, 2006. "Testing Covariance Stationarity," Economics Working Papers (Ensaios Economicos da EPGE) 632, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
  2. Wagner P. Gaglianone & Luiz Renato Lima & Oliver Linton, 2008. "Evaluating Value-at-Risk Models via Quantile Regressions," Working Papers Series 161, Central Bank of Brazil, Research Department. [Downloadable!]
  3. Luiz Renato Regis de Oliveira Lima & Raquel M. B. Sampaio & Wagner Piazza Gaglianone, 2006. "Debt ceiling and fiscal sustainability in Brazil: a quantile autoregression approach," Economics Working Papers (Ensaios Economicos da EPGE) 631, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
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