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Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors

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  • Jan G. De Gooijer

    (University of Amsterdam)

Abstract

This study explores the multi-step ahead forecasting performance of a so-called hybrid conditional quantile method, which combines relevant conditional quantile forecasts from parametric and semiparametric methods. The focus is on lower (left) and upper (right) tail quantiles of the conditional distribution of the response variable. First, we evaluate and compare out-of-sample conditional quantile forecasts obtained from a hybrid method and from five non-hybrid methods, employing a large data set of exogenous predictors generated by various GARCH model specifications. Second, we compare the accuracy of these methods by calculating conditional quantile forecasts for the risk premium of the monthly S&P 500 index, using a data set of macroeconomic predictors. Monte Carlo and empirical application results indicate that the hybrid forecasting provides more accurate quantile forecasts than non-hybrid methods. The success of the hybrid method is most prominent when compared with results obtained by a simple equal-weighted combination of quantile forecasts.

Suggested Citation

  • Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10289-9
    DOI: 10.1007/s10614-022-10289-9
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    1. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.
    2. Xiao, Zhijie & Koenker, Roger, 2009. "Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1696-1712.
    3. Yoshimasa Uematsu & Shinya Tanaka, 2019. "High†dimensional macroeconomic forecasting and variable selection via penalized regression," The Econometrics Journal, Royal Economic Society, vol. 22(1), pages 34-56.
    4. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    5. Schwert, G William, 1990. "Stock Volatility and the Crash of '87," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 77-102.
    6. Loukia Meligkotsidou & Ekaterini Panopoulou & Ioannis D. Vrontos & Spyridon D. Vrontos, 2014. "A Quantile Regression Approach to Equity Premium Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 558-576, November.
    7. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    8. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    9. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    10. Loukia Meligkotsidou & Ekaterini Panopoulou & Ioannis D. Vrontos & Spyridon D. Vrontos, 2019. "Quantile forecast combinations in realised volatility prediction," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1720-1733, October.
    11. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    12. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    13. Cenesizoglu, Tolga & Timmermann, Allan, 2012. "Do return prediction models add economic value?," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2974-2987.
    14. Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
    15. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.
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    More about this item

    Keywords

    Encompassing; Hybridization; Penalized quantile averaging; Quantile forecasting; Tick loss function;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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