<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
<p>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.</p>
</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.

Combining forecasts? Keep it simple

Central European Economic Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Combining forecasts? Keep it simple"}

This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR) modeling, aiming to enhance accuracy and...

Combining forecasts? Keep it simple

Online veröffentlicht: 31. Okt. 2023

Seitenbereich: 343 - 370

DOI: https://doi.org/10.2478/ceej-2023-0020

Schlüsselwörter
Machine learning, GARCH models, combined forecasts, commodities, VaR

© 2023 Szymon Lis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Combining forecasts? Keep it simple

Online veröffentlicht: 31. Okt. 2023

Seitenbereich: 343 - 370

DOI: https://doi.org/10.2478/ceej-2023-0020

SchlüsselwörterMachine learning, GARCH models, combined forecasts, commodities, VaR

© 2023 Szymon Lis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Machine learning, GARCH models, combined forecasts, commodities, VaR