Testing interval forecasts: a GMM-based approach
This paper proposes a new evaluation framework for interval forecasts. Our model free test can be used to evaluate intervals forecasts and High Density Regions, potentially discontinuous and/or asymmetric. Using a simple J-statistic, based on the moments de ned by the orthonormal polynomials associated with the Binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte-Carlo simulations show that for realistic sample sizes, our GMM test has good small-sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It con rms that using this GMM test leads to major consequences for the ex-post evaluation of interval forecasts produced by linear versus nonlinear models.
|Date of creation:||Aug 2011|
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- Wallis, Kenneth F., 2002.
"Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts,"
Royal Economic Society Annual Conference 2002
181, Royal Economic Society.
- Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
- Wallis, Kenneth F., 2001. "Chi-squared tests of interval and density forecasts and the Bank of England's fan charts," Working Paper Series 0083, European Central Bank.
- Christian Bontemps & Nour Meddahi, 2002.
"Testing Normality: A GMM Approach,"
CIRANO Working Papers
- Candelon Bertrand & Colletaz Gilberg & Hurlin Christophe & Tokpavi Sessi, 2009.
"Backtesting Value-at-Risk: A GMM Duration-based Test,"
051, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(2), pages 314-343, Spring.
- Gilbert COLLETAZ & Christophe HURLIN & Sessi TOKPAVI, 2008. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Working Papers 266, Orleans Economic Laboratorys, University of Orleans.
- Gilbert COLLETAZ & Christophe HURLIN & Sessi TOKPAVI, 2009. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Working Papers 265, Orleans Economic Laboratorys, University of Orleans.
- Christophe Hurlin & Gilbert Colletaz & Sessi Tokpavi & Bertrand Candelon, 2008. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Working Papers halshs-00329495, HAL.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005.
"Evaluating Value-at-Risk models with desk-level data,"
Working Paper Series
010, North Carolina State University, Department of Economics, revised Dec 2006.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
- Peter Christoffersen & Jeremy Berkowitz & Denis Pelletier, 2008. "Evaluating Value-at-Risk Models with Desk-Level Data," CREATES Research Papers 2009-35, School of Economics and Management, University of Aarhus.
- Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
- Michael P. Clements & Nick Taylor, 2003. "Evaluating interval forecasts of high-frequency financial data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 445-456.
- David I. Harvey & Stephen J. Leybourne, 2007. "Testing for time series linearity," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 149-165, 03.
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