Design of new energy market indicator system and dynamic risk assessment based on graph neural network: enhancing market monitoring and forecasting capability
, , e
17 mar 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 17 mar 2025
Ricevuto: 25 ott 2024
Accettato: 04 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0165
Parole chiave
© 2025 Xiaolu Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.

The GARCH model parameter estimation result
Shenzhen carbon market | Hubei carbon market | Coal market | Crude oil market | New energy market | |
0.0352 | -0.0351 | 0.0642 | 0.0724 | 0.0507 | |
1.8579 | 0.5628 | 0.0597 | 0.2458 | 0.0214 | |
0.3264 | 0.6587 | 0.0928 | 0.1468 | 0.0575 | |
0.6175 | 0.3051 | 0.8994 | 0.8325 | 0.9267 | |
AIC | 7.6814 | 4.5007 | 3.6125 | 4.6119 | 3.9865 |
BIC | 7.7789 | 4.1236 | 4.0437 | 4.7325 | 3.8714. |
Selection of copula functions
Gaussion Copula | Student-t Copula | Frank Copula | ||
Shenzhen → new energy | LogLike | 0.52 | 1.08 | 0.25 |
AIC | 0.91 | 2.31 | 1.68 | |
BIC | 7.04 | 15.27 | 8.34 | |
Hubei → new energy | LogLike | 0.25 | -0.15 | 0.51 |
AIC | 1.64 | 5.93 | 1.19 | |
BIC | 7.79 | 16.34 | 7.53 |
The Ljung-Box Q test of the GARCH(1, 1) model
Market | Ljung-Box | P VALUE |
Shenzhen carbon market | 0.0067 | 0.9537 |
Hubei carbon market | 1.2543 | 0.2856 |
Coal market | 0.1895 | 0.6979 |
Crude oil market | 0.9677 | 0.3508 |
New energy market | 0.9568 | 0.3641 |
The prediction error of the Shanghai 50 index
MAE | MAPE | RMSE | ||
Shanghai Shanghai 50 index | GNN | 1.125036 | 0.000542 | 9.326473 |
LSTM | 4.653381 | 0.001779 | 38.490865 | |
Random forest | 1.560437 | 0.000459 | 14.527119 | |
XGBoost | 0.983642 | 0.000406 | 8.667859 |
Comparative analysis
Prediction model | GNN | SVM | ||||||
Hysteresis | First-order | Second order | Third order | Fourth-order | First-order | Second order | Third order | Fourth-order |
MAPE/% | 3.52 | 4.14 | 4.96 | 5.54 | 3.78 | 4.25 | 4.92 | 5.76 |
MAE | 0.756 | 0.824 | 0.945 | 1.354 | 0.757 | 0.824 | 0.927 | 1.288 |
RMSE | 0.598 | 0.635 | 0.778 | 0.897 | 0.611 | 0.676 | 0.790 | 0.954 |
FVD | 0.891 | 0.837 | 0.775 | 0.714 | 0.837 | 0.797 | 0.785 | 0.729 |
Prediction model | GM(1,N) | ARMAX | ||||||
Hysteresis | First-order | Second order | Third order | Fourth-order | First-order | Second order | Third order | Fourth-order |
MAPE/% | 4.58 | 5.96 | 5.27 | 6.35 | 6.59 | 7.57 | 8.04 | 8.59 |
MAE | 0.896 | 0.974 | 1.345 | 1.578 | 1.781 | 1.995 | 1.653 | 1.907 |
RMSE | 0.665 | 0.725 | 0.787 | 0.965 | 0.965 | 0.993 | 1.042 | 1.214 |
FVD | 0.824 | 0.810 | 0.726 | 0.722 | 0.776 | 0.721 | 0.807 | 0.753 |
Prediction model | BP | RM | ||||||
Hysteresis | First-order | Second order | Third order | Fourth-order | First-order | Second order | Third order | Fourth-order |
MAPE/% | 7.89 | 8.65 | 9.75 | 10.24 | 9.03 | 9.75 | 10.03 | 11.48 |
MAE | 1.635 | 1.962 | 2.653 | 2.369 | 2.214 | 2.324 | 2.635 | 2.989 |
RMSE | 0.797 | 0.804 | 0.979 | 1.324 | 0.706 | 0.927 | 1.333 | 1.502 |
FVD | 0.735 | 0.747 | 0.614 | 0.685 | 0.724 | 0.643 | 0.659 | 0.657 |
Shenzhen, hubei carbon market and energy market risk spill value (q= 0_05)
CoVaR | ΔCoVaR | %ΔCoVaR | |
Shenzhen carbon market → new energy market | -3.654 | 0.379 | 8.76% |
Hubei carbon market → new energy market | -3.245 | 0.197 | 6.35% |
New energy market → shenzhen carbon market | -25.964 | 3.564 | 14.72% |
New energy market → hubei carbon market | -4.012 | 0.758 | 16.34% |
The risk spilt on the carbon market and energy markets (q= 0_05)
CoVaR | ΔCoVaR | %ΔCoVaR | |
Carbon markets, new energy markets | -3.596 | 0.348 | 7.86% |
New energy market, carbon market | -13.078 | 2.053 | 16.35% |