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Evaluation and Combination of Conditional Quantile Forecasts

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Author Info
Raffaella Giacomini () (Boston College)
Ivana Komunjer () (California Institute of Technology)

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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.

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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 571.

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Length: 43 pages
Date of creation: 01 Jun 2003
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Handle: RePEc:boc:bocoec:571

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Related research
Keywords: Encompassing; Loss Function; GMM; Value at Risk;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
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
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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. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  2. Maria Rosa Nieto & Esther Ruiz, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," Statistics and Econometrics Working Papers ws087326, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  3. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104. [Downloadable!]
    Other versions:
  4. Gaglianone, Wagner Piazza & Linton, Oliver & Lima, Luiz Renato Regis de Oliveira, 2008. "Evaluating Value-at-Risk models via Quantile regressions," Economics Working Papers (Ensaios Economicos da EPGE) 679, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
    Other versions:
  5. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics. [Downloadable!]
  6. Huiyu Huang & Tae-Hwy Lee, 2006. "To Combine Forecasts or to Combine Information?," Working Papers 200806, University of California at Riverside, Department of Economics, revised Feb 2009. [Downloadable!]
  7. J. Carlos Escanciano & Jose Olmo, 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," City University Economics Discussion Papers 07/11, Department of Economics, City University, London. [Downloadable!]
  8. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," Caepr Working Papers 2007-005_updated, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington. [Downloadable!]
  9. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  10. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  11. Raffaella Giacomini & Halbert White, 2003. "Tests of conditional predictive ability," Boston College Working Papers in Economics 572, Boston College Department of Economics. [Downloadable!]
    Other versions:
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