Forecasting value-at-risk by encompassing CAViaR models via information criteria

다국어 초록 (Multilingual Abstract)

This paper proposes a new method of VaR forecasting using the conditional au-toregressive 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.

참고문헌 (Reference)

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