Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach
Abstract
:1. Introduction
2. Model Specification and Estimation
2.1. TVP-VAR-SV Model for Price Dynamics
2.2. Volatility Spillover Measures and Graph Network
2.3. Multivariate Nonlinear Causality and Impulse Response
3. Data
4. Empirical Analysis in China’s Rare Earth Market
4.1. Risk Spillover of China’s Rare Earth Market
4.2. Bilateral Trading Risk Spillover Complex Network
4.3. Risk Spillover Mechanism and Driven Factor Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
- Give the initial value of , , , and ;
- Sample from , subject to the given condition , , and ;Specifically, the observation equation corresponds to the linear Gaussian shocks of equation. For the observable vector , the state vector is and the set of the parameter vector is . We have the following conditional transformation, respectively:Finally, with the mean and derivative of and , the conditional density function can be expressed as:
- Sample from , subject to the given condition , , and ;
- Sample from , subject to the given condition , , and . Specifically, sample the independent identically distributed random variables Q, W, and S from ;
- Sample from , subject to the given condition , , and ;
- Return to 2.
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Name | RECI | Catalysts | Praseodymium | Holmium | VXFXI | EPU | EER | Liquidity | |
---|---|---|---|---|---|---|---|---|---|
Info | |||||||||
Observation | 1249 | 1249 | 1249 | 1249 | 1249 | 1249 | 1249 | 1249 | |
Frequency | Daily | Daily | Daily | Daily | Daily | Daily | Daily | Daily | |
Mean | 0.000 | −0.000 | 0.000 | 0.001 | 0.002 | 0.193 | 0.000 | 0.034 | |
Minimum | −0.040 | −0.045 | −0.095 | −0.105 | −0.184 | −0.839 | −0.014 | −1.951 | |
Maximum | 0.045 | 0.046 | 0.081 | 0.124 | 0.418 | 7.264 | 0.010 | 16.592 | |
1st Quartile | −0.003 | −0.003 | −0.002 | −0.002 | −0.033 | −0.309 | −0.001 | −0.029 | |
3rd Quartile | 0.003 | 0.003 | 0.003 | 0.003 | 0.028 | 0.437 | 0.001 | 0.030 | |
Variance | 0.000 | 0.000 | 0.000 | 0.000 | 0.004 | 0.639 | 0.000 | 0.434 | |
S.D. | 0.007 | 0.010 | 0.013 | 0.015 | 0.063 | 0.800 | 0.002 | 0.659 | |
Skewness | 0.228 | 0.028 | −0.329 | 0.555 | 1.275 | 2.396 | −0.100 | 19.971 | |
Kurtosis | 6.732 | 5.447 | 13.417 | 14.309 | 5.103 | 10.150 | 3.002 | 449.406 | |
J–B | |||||||||
ARCH-LM | |||||||||
Q(20) | 19.193 | ||||||||
ADF(10) | |||||||||
K–S | 0.022 | 0.038 | 0.019 | 0.020 | 0.017 | 0.015 | 0.030 | 0.014 | |
Explanation | China’s rare earth composite index | Catalysts index | Rare earth oxides | Rare earth oxides | ETF volatility index of China | Economic policy uncertainty | Effective exchange rate | 3M-Spread of SHIBOR and treasury yield | |
Source | Wind [31] | Wind [31] | Wind [31] | Wind [31] | Wind [31] | BIS [32] | Du et al. [29] | Wind [31] |
RECI | Cata. | Hydr. | Lumi. | Magn. | Gado. | Yttr. | Sama. | Holm. | Erbi. | Euro. | Thul. | Terb. | Dysp. | Lute. | Pras. | Ytte. | FROM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RECI | 19.40 | 7.04 | 6.03 | 6.00 | 15.46 | 4.23 | 2.58 | 2.38 | 3.37 | 3.48 | 3.23 | 1.26 | 3.64 | 4.60 | 2.76 | 12.85 | 1.68 | 80.60 |
Cata. | 7.54 | 31.60 | 13.00 | 4.21 | 7.12 | 2.13 | 10.64 | 3.04 | 1.69 | 2.72 | 0.73 | 1.23 | 1.78 | 2.06 | 2.26 | 6.61 | 1.62 | 68.40 |
Hydr. | 7.57 | 11.23 | 29.01 | 1.58 | 15.11 | 2.76 | 1.48 | 1.95 | 1.61 | 1.19 | 1.05 | 0.84 | 1.19 | 1.01 | 1.77 | 19.52 | 1.16 | 70.99 |
Lumi. | 8.91 | 4.62 | 1.60 | 30.80 | 3.11 | 1.89 | 8.61 | 1.55 | 2.25 | 2.38 | 16.92 | 1.14 | 3.30 | 1.97 | 6.84 | 2.85 | 1.25 | 69.20 |
Magn. | 11.16 | 7.93 | 12.58 | 1.43 | 25.80 | 2.99 | 1.30 | 2.31 | 1.54 | 1.54 | 0.88 | 1.44 | 1.61 | 2.15 | 1.98 | 22.04 | 1.31 | 74.20 |
Gado. | 6.84 | 3.77 | 3.54 | 1.82 | 8.52 | 39.94 | 2.96 | 2.91 | 2.36 | 2.95 | 1.43 | 2.02 | 3.15 | 2.56 | 2.73 | 9.03 | 3.47 | 60.06 |
Yttr. | 4.74 | 13.75 | 2.08 | 10.43 | 3.51 | 2.61 | 39.41 | 2.78 | 2.07 | 3.31 | 1.21 | 1.83 | 2.26 | 2.13 | 2.28 | 4.07 | 1.53 | 60.59 |
Sama. | 4.02 | 4.75 | 3.63 | 1.88 | 4.83 | 2.78 | 3.06 | 49.88 | 2.35 | 3.95 | 1.08 | 2.46 | 2.27 | 2.96 | 2.19 | 4.77 | 3.15 | 50.12 |
Holm. | 7.98 | 2.94 | 3.54 | 2.83 | 8.09 | 3.71 | 2.50 | 2.36 | 40.11 | 2.86 | 2.23 | 1.76 | 3.24 | 2.11 | 3.32 | 8.81 | 1.62 | 59.89 |
Erbi. | 5.91 | 3.05 | 1.62 | 2.99 | 4.03 | 2.54 | 3.46 | 3.08 | 2.88 | 51.44 | 1.89 | 1.99 | 2.50 | 2.68 | 3.33 | 4.69 | 1.93 | 48.56 |
Euro. | 5.71 | 1.30 | 1.65 | 23.54 | 2.79 | 1.98 | 1.53 | 1.15 | 1.83 | 2.09 | 44.99 | 0.88 | 1.47 | 2.00 | 2.67 | 3.13 | 1.27 | 55.01 |
Thul. | 2.76 | 2.89 | 1.99 | 1.87 | 4.38 | 2.95 | 2.69 | 3.58 | 1.68 | 2.34 | 1.44 | 56.11 | 2.04 | 2.21 | 2.18 | 5.08 | 3.80 | 43.89 |
Terb. | 8.83 | 2.61 | 1.87 | 4.98 | 6.96 | 4.20 | 2.61 | 2.08 | 2.98 | 2.56 | 1.83 | 1.41 | 43.05 | 3.20 | 2.70 | 6.13 | 2.01 | 56.95 |
Dysp. | 10.53 | 2.80 | 2.03 | 2.59 | 6.73 | 3.82 | 2.73 | 2.46 | 2.72 | 3.06 | 2.18 | 1.85 | 3.13 | 44.19 | 2.72 | 5.04 | 1.42 | 55.81 |
Lute. | 4.25 | 3.44 | 2.31 | 8.05 | 6.50 | 2.89 | 2.56 | 2.27 | 2.36 | 3.26 | 2.04 | 1.35 | 2.45 | 1.97 | 43.71 | 7.36 | 3.22 | 56.29 |
Pras. | 6.97 | 7.43 | 17.10 | 1.15 | 22.10 | 2.71 | 1.00 | 2.08 | 0.97 | 1.41 | 0.89 | 1.18 | 1.27 | 1.04 | 1.99 | 29.32 | 1.39 | 70.68 |
Ytte. | 3.41 | 3.45 | 1.89 | 2.02 | 3.49 | 4.10 | 2.17 | 3.75 | 1.89 | 1.85 | 1.42 | 3.81 | 2.72 | 1.87 | 3.67 | 4.18 | 54.31 | 45.69 |
TO | 107.14 | 83.00 | 76.46 | 77.39 | 122.72 | 48.30 | 51.90 | 39.73 | 34.54 | 40.95 | 40.43 | 26.45 | 38.01 | 36.53 | 45.39 | 126.17 | 31.84 | 64.18/60.41 |
VXFXI | EER | EPU | Liquidity | |
---|---|---|---|---|
Panel A (: RECI is not the macroeconomic variables’ nonlinear Granger causality reason) | ||||
Statistics | 0.5967 | −1.6102 | ||
p-Value | 0.2753 | 0.0098 | 0.0983 | 0.9463 |
Panel B (: Macroeconomic variables are not the RECI’s nonlinear Granger causality reason) | ||||
Statistics | ||||
p-Value | 0.0002 | 0.0000 | 0.0234 | 0.0781 |
Mean | S.D. | 95% C.I. | Geweke | Const. | |
---|---|---|---|---|---|
0.0023 | 0.0003 | [0.0018, 0.0029] | 0.6720 | 3.0600 | |
0.0023 | 0.0003 | [0.0018, 0.0029] | 0.0040 | 3.7300 | |
0.0056 | 0.0016 | [0.0034, 0.0097] | 0.7000 | 32.0000 | |
0.0055 | 0.0016 | [0.0034, 0.0094] | 0.7390 | 17.2000 | |
0.0022 | 0.0010 | [0.0016, 0.0057] | 0.1240 | 274.9900 | |
0.0052 | 0.0017 | [0.0019, 0.0092] | 0.0000 | 94.0100 |
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Ye, R.; Gong, J.; Xia, X. Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach. Systems 2023, 11, 168. https://doi.org/10.3390/systems11040168
Ye R, Gong J, Xia X. Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach. Systems. 2023; 11(4):168. https://doi.org/10.3390/systems11040168
Chicago/Turabian StyleYe, Rendao, Jincheng Gong, and Xinting Xia. 2023. "Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach" Systems 11, no. 4: 168. https://doi.org/10.3390/systems11040168