- Tytuł:
- Combining forecasts? Keep it simple
- Autorzy:
-
Lis, Szymon
Chlebus, Marcin - Powiązania:
- https://bibliotekanauki.pl/articles/22443122.pdf
- Data publikacji:
- 2023-10-31
- Wydawca:
- Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
- Tematy:
-
Machine learning
GARCH model
combined forecasts
commodities
VaR - Opis:
- 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 forgold, 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.
- Źródło:
-
Central European Economic Journal; 2023, 10, 57; 343-370
2543-6821 - Pojawia się w:
- Central European Economic Journal
- Dostawca treści:
- Biblioteka Nauki
Artykuł