Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model
Abstract
:1. Introduction
2. Methodology
2.1. Multivariate Empirical Mode Decomposition and Convolutional Neural Network
2.2. MEMD-CNN VaR Model
2.3. Data Collection
3. Empirical Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Markets | Standard Deviation | Skewness | Kurtosis | |||||
---|---|---|---|---|---|---|---|---|
13.8424 | 0.0238 | −0.0229 | 7.4014 | 0.001 | 0 | 1 | 0.9742 | |
18.9063 | 0.0215 | 0.0882 | 7.4790 | 0.001 | 0 | 0.9742 | 1 | |
2.0736 | 0.0061 | 0.1 | 5.6425 | 0.001 | 0 | 0.7391 | 0.6677 | |
20.7152 | 0.0108 | −0.1548 | 13.7536 | 0.001 | 0 | −0.0212 | 0.0446 |
Markets | ||||||||
---|---|---|---|---|---|---|---|---|
2.6890 | 6890 | 2.6893 | 2.6882 | 2.6892 | 2.7165 | 2.6906 | 2.6891 | |
3.1898 | 3.1898 | 3.1901 | 3.1891 | 3.1903 | 3.2194 | 3.1858 | 3.1817 | |
3.8816 | 3.8817 | 3.8820 | 3.8812 | 3.8824 | 3.9141 | 3.8712 | 3.8644 | |
2.0657 | 2.0664 | 2.0660 | 2.0751 | 2.0871 | 2.0706 | 2.1003 | 2.0818 | |
2.5292 | 2.5300 | 2.5295 | 2.5342 | 2.5481 | 2.5357 | 2.5637 | 2.5457 | |
3.1654 | 3.1663 | 3.1657 | 3.1651 | 3.1813 | 3.1740 | 3.1995 | 3.1828 |
Markets | ||||||||
---|---|---|---|---|---|---|---|---|
2.6890 | 2.6889 | 2.6894 | 2.6923 | 2.6940 | 2.7587 | 2.6818 | 2.7465 | |
3.1898 | 3.1897 | 3.1904 | 3.1941 | 3.1943 | 3.2612 | 3.1715 | 3.2347 | |
3.8817 | 3.8816 | 3.8824 | 3.8874 | 3.8856 | 3.9551 | 3.8506 | 3.9121 | |
2.0702 | 2.0706 | 2.0712 | 2.0705 | 2.0995 | 2.1311 | 2.1261 | 2.1318 | |
2.5350 | 2.5355 | 2.5362 | 2.5354 | 2.5582 | 2.5975 | 2.5832 | 2.5965 | |
3.1728 | 3.1734 | 3.1743 | 3.1733 | 3.1888 | 3.2371 | 3.2120 | 3.2343 |
Model | |||||||||
---|---|---|---|---|---|---|---|---|---|
22 | 10 | 6 | 0.3492 | 0.7401 | 0.0054 | 1.4780 | 1.8740 | 2.4289 | |
29 | 13 | 9 | 0.0142 | 0.2050 | 0.0163 | 1.4298 | 1.7568 | 2.2135 | |
17 | 9 | 6 | 0.8072 | 1 | 0.2460 | 1.4295 | 1.7562 | 2.2128 | |
24 | 14 | 4 | 0.1665 | 0.1181 | 0.8351 | 1.1621 | 1.5143 | 2.0077 | |
17 | 7 | 2 | 0.8072 | 0.4826 | 0.3549 | 1.1050 | 1.4067 | 1.8299 | |
17 | 7 | 2 | 0.8072 | 0.4826 | 0.3549 | 1.1049 | 1.4066 | 1.8297 |
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Zou, Y.; He, K. Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model. Mathematics 2022, 10, 2413. https://doi.org/10.3390/math10142413
Zou Y, He K. Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model. Mathematics. 2022; 10(14):2413. https://doi.org/10.3390/math10142413
Chicago/Turabian StyleZou, Yingchao, and Kaijian He. 2022. "Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model" Mathematics 10, no. 14: 2413. https://doi.org/10.3390/math10142413
APA StyleZou, Y., & He, K. (2022). Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model. Mathematics, 10(14), 2413. https://doi.org/10.3390/math10142413