An improved model accuracy for forecasting risk measures: application of ensemble methods
Statistical-based predictions with extreme value theory improve the performance of the risk model not by choosing the model structure that is expected to predict the best but by developing a model whose results are a combination of models with different shapes. Using different ensemble algorithms to...
        Saved in:
      
    
          | Main Authors: | Katleho Makatjane, Kesaobaka Mmelesi | 
|---|---|
| Format: | Article | 
| Language: | English | 
| Published: | Taylor & Francis Group
    
        2024-12-01 | 
| Series: | Journal of Applied Economics | 
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15140326.2024.2395775 | 
| Tags: | Add Tag 
      No Tags, Be the first to tag this record!
   | 
Similar Items
- 
                
                    Electricity Demand Forecasting of Value-at-Risk and Expected Shortfall: The South African Context        
                          
 by: Bofelo Moemedi Masilo, et al.
 Published: (2024-12-01)
- 
                
                    Modelling and forecasting mobile money customer transaction volumes in rural and semi-urban Malawi: An autoregressive integrated moving average spatial decomposition        
                          
 by: Danny Namakhwa, et al.
 Published: (2024-12-01)
- 
                
                    Do shocks to electricity consumption generate persistent effect? Evidence from Hunan Province in China        
                          
 by: Sheng Xiang, et al.
 Published: (2025-01-01)
- 
                
                    Gauging the dynamic interlinkage among robotics, artificial intelligence, and green crypto investment: A quantile VAR approach        
                          
 by: Le Thanh Ha
 Published: (2024-12-01)
- 
                
                    TXtreme: transformer-based extreme value prediction framework for time series forecasting        
                          
 by: Hemant Yadav, et al.
 Published: (2025-01-01)
 
       