Numerical comparison of multivariate models to forecasting risk measures
Fernanda Maria Müller () and
Marcelo Brutti Righi ()
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Fernanda Maria Müller: Federal University of Rio Grande do Sul
Marcelo Brutti Righi: Federal University of Rio Grande do Sul
Risk Management, 2018, vol. 20, issue 1, 29-50
Abstract We evaluated the performance of multivariate models for forecasting Value at Risk (VaR), Expected Shortfall (ES), and Expectile Value at Risk (EvaR). We used Historical Simulation (HS), Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedastic (DCC-GARCH) and copula methods: Regular copulas, Vine copulas, and Nested Archimedean copulas (NAC). We assessed the performance of the models using Monte Carlo simulations, considering different scenarios, regarding the marginal distributions, correlation, and number of portfolio assets. Numerical results evidenced the accuracy forecasting risk measures are associated with marginal distributions. For a data-generating process where the marginal distribution is Gaussian, Regular and Vine copulas demonstrated better performance. For data generated with Student’s t distribution, we verified better performance by NAC. In addition, we identified the superiority of copula methods over HS and DCC-GARCH, which reduces the model risk.
Keywords: Risk measures; DCC-GARCH; Copulas; Model risk; Monte Carlo simulation (search for similar items in EconPapers)
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