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- W2095581808 abstract "This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification." @default.
- W2095581808 created "2016-06-24" @default.
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- W2095581808 date "2013-11-30" @default.
- W2095581808 modified "2023-10-05" @default.
- W2095581808 title "Forecasting value-at-risk by encompassing CAViaR models via information criteria" @default.
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- W2095581808 doi "https://doi.org/10.7465/jkdi.2013.24.6.1531" @default.
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