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Combining forecasts? Keep it simple

Author

Listed:
  • Lis Szymon

    (University of Warsaw, Faculty of Economic Sciences, 44/50 Długa street, 00-241 Warsaw, Poland)

  • Chlebus Marcin

    (University of Warsaw, Faculty of Economic Sciences, 44/50 Długa street, 00-241 Warsaw, Poland)

Abstract

This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR) modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 for gold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure. Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.

Suggested Citation

  • Lis Szymon & Chlebus Marcin, 2023. "Combining forecasts? Keep it simple," Central European Economic Journal, Sciendo, vol. 10(57), pages 343-370, January.
    • Handle: RePEc:vrs:ceuecj:v:10:y:2023:i:57:p:343-370:n:11
    DOI: 10.2478/ceej-2023-0020
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    References listed on IDEAS

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    1. 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.
    2. Danielsson, Jon & Morimoto, Yuji, 2000. "Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 25-48, December.
    3. Yiuman Tse, 2016. "Asymmetric Volatility, Skewness, and Downside Risk in Different Asset Classes: Evidence from Futures Markets," The Financial Review, Eastern Finance Association, vol. 51(1), pages 83-111, February.
    4. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    5. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development

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