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Evaluation and Combination of Conditional Quantile Forecasts

Author

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  • Raffaella Giacomini

    () (Boston College)

  • Ivana Komunjer

    () (California Institute of Technology)

Abstract

This paper proposes a method for comparing and combining conditional quantile forecasts in an out-of-sample framework. We construct a Conditional Quantile Forecast Encompassing (CQFE) test as a Wald-type test of superior predictive ability. Rejection of CQFE provides a basis for combination of conditional quantile forecasts. Two central features of our implementation of the principle of encompassing are, first, the use of the 'tick' loss function and, second, a conditional, rather than unconditional approach to out-of-sample evaluation. Some of the advantages of the conditional approach are that it allows the forecasts to be generated by using general estimation procedures and that it is applicable when the forecasts are based on both nested and non-nested models. The test is also relatively easy to implement using standard GMM techniques. An empirical application to Value-at-Risk evaluation illustrates the usefulness of our method.

Suggested Citation

  • Raffaella Giacomini & Ivana Komunjer, 2003. "Evaluation and Combination of Conditional Quantile Forecasts," Boston College Working Papers in Economics 571, Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:571
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    References listed on IDEAS

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    More about this item

    Keywords

    Encompassing; Loss Function; GMM; Value at Risk;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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