Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts

Szymon Lis () and Marcin Chlebus ()
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Szymon Lis: Faculty of Economic Sciences, University of Warsaw

No 2021-11, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: No model dominates existing VaR forecasting comparisons. This problem may be solved by combine forecasts. This study investigates the daily volatility forecasting for commodities (gold, silver, oil, gas, copper) from 2000-2020 and identifies the source of performance improvements between individual GARCH models and combining forecasts methods (mean, the lowest, the highest, CQOM, quantile regression with the elastic net or LASSO regularization, random forests, gradient boosting, neural network) through the MCS. Results indicate that individual models achieve more accurate VaR forecasts for the confidence level of 0.975, but combined forecasts are more precise for 0.99. In most cases simple combining methods (mean or the lowest VaR) are the best. Such evidence demonstrates that combining forecasts is important to get better results from the existing models. The study shows that combining the forecasts allows for more accurate VaR forecasting, although it’s difficult to find accurate, complex methods.

Keywords: Combining forecasts; Econometric models; Finance; Financial markets; GARCH models; Neural networks; Regression; Time series; Risk; Value-at-Risk; Machine learning; Model Confidence Set (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 G32 Q01 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ets, nep-for, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-11

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