Research into the Mechanism for the Impact of Climate Change on Systemic Risk—A Case Study of China’s Small- and Medium-sized Commercial Banks
Abstract
:1. Introduction
2. Analysis of the Mechanism for the Effect of Climate Change on Changes in Financial Risks
2.1. Direct Impact of Climate Change on Changes in Financial Risks
2.2. Indirect Impact of Climate Change on Changes in Financial Risks
3. Measurement of Systemic Financial Risks and Climate Change
3.1. Measurement of Systemic Financial Risks Based on the Dynamic CoVaR Model
3.1.1. Introduction to Model
3.1.2. Data Selection and Processing
3.1.3. Status Variables
3.1.4. Empirical Results
3.2. Measurement of Risk caused by Climate Change
3.2.1. Selection of Indicators
3.2.2. Data Processing and Analysis
4. Assessment of the Impact of Climate Risk on Systemic Risk in Commercial Banks
4.1. Regression Model Based on the Single-Factor Impact of Climate
4.1.1. Creation of Inter-Series Cointegration
4.1.2. Error Correction Model
4.2. Building a Regression Model of Multiple-Factor Impact
4.2.1. Research Hypothesis
4.2.2. Model Building
4.2.3. Empirical Research and Result Analysis
5. Discussion
5.1. The Main Innovation of This Study
5.2. Countermeasure Suggestions for Banks and Government
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Banks | |||||||
---|---|---|---|---|---|---|---|
China Minsheng Bank | 0.856345 (30.09) | 0.2563 | −0.1219 | 13.8702 | 0.6294 | −3.4297 | 6.0078 |
Huaxia Bank | 0.685290 (28.14) | 8.0419 | 0.0316 | 8.3495 | 8.9260 | 0.8924 | −7.2788 |
Bank of Beijing | 0.765317 (25.08) | −0.1324 | −0.4479 | 4.9239 | 0.2894 | −2.1921 | 0.4153 |
Bank of Nanjing | 0.650929 (10.06) | −0.9238 | −0.1688 | 3.1751 | −3.0075 | −2.7424 | −7.8969 |
Bank of Ningbo | 0.711595 (11.14) | 0.7706 | −0.1713 | 3.9613 | 1.0962 | −2.8082 | −3.9274 |
Ping An Bank | 0.668712 (29.18) | 4.2248 | 0.1875 | 13.9360 | −0.2058 | −5.3534 | 6.6150 |
Shanghai Pudong Development Bank | 0.723375 15.78) | 1.1720 | −0.3501 | 7.4879 | −0.6822 | −5.3691 | 13.1230 |
Industrial Bank | 0.798520 (33.31) | 5.5964 | 0.4517 | 11.5811 | 6.6687 | −2.5132 | 0.5325 |
China Merchants Bank | 0.757308 (30.24) | 0.5868 | −0.1320 | 3.7779 | 1.6247 | 0.5291 | −5.9086 |
China CITIC Bank | 0.810208 (20.67) | 13.6422 | 0.1862 | 11.2629 | 13.2790 | −1.5200 | −11.3298 |
China Everbright Bank | 0.774710 (28.55) | 9.0757 | 0.1234 | 9.4735 | 7.5043 | −1.8522 | −6.7870 |
Banks | |||||||
---|---|---|---|---|---|---|---|
China Minsheng Bank | - | −1.1966 | −0.0518 | −1.0987 | −0.9090 | −0.3519 | 1.6229 |
Huaxia Bank | - | −0.7739 | −0.1368 | −1.1132 | −0.2164 | −0.1428 | 1.0374 |
Bank of Beijing | - | −0.7974 | −0.1307 | −0.7848 | −0.7721 | −0.2203 | 0.6553 |
Bank of Nanjing | - | −0.2471 | −0.0778 | −0.8082 | −0.0720 | −0.2023 | 0.4323 |
Bank of Ningbo | - | −1.2653 | −0.1016 | −1.2730 | −1.1757 | −0.3384 | 1.1455 |
Ping An Bank | - | −0.9791 | −0.0801 | −1.6228 | −0.7318 | −0.1281 | 0.9631 |
Shanghai Pudong Development Bank | - | −0.7994 | −0.0929 | −1.0020 | −0.5843 | −0.4944 | 0.8505 |
Industrial Bank | - | −1.0974 | −0.1585 | −1.3098 | −0.8411 | −0.4610 | 1.2712 |
China Merchants Bank | - | −1.0088 | −0.1553 | −1.1124 | −0.7291 | −0.3858 | 1.1022 |
China CITIC Bank | - | −0.9109 | −0.0312 | −1.0315 | −0.6562 | −0.5171 | 1.8194 |
China Everbright Bank | - | 0.1697 | −0.0970 | −1.1801 | 0.3483 | −0.1299 | −0.7992 |
Variable | Test Form | t-Statistics | 10% Threshold | Prob * | Results |
---|---|---|---|---|---|
(c,t,0) | −4.020948 | −2.580908 | 0.0019 | Smooth | |
DP | (c,t,0) | −2.629114 | −2.580908 | 0.0902 | Smooth |
Coefficient | t-Statistics | p Value | R2 | DW Value | |
---|---|---|---|---|---|
Constant | 9.0372 | 1.906523 | 0.0983 | 0.24 | 0.5234 |
Dp | 0.6229 | 1.817289 | 0.0652 |
Residual | ADF Test Value | 10% Threshold | Results |
---|---|---|---|
ε( −Dp) | −4.0391 | −2.8879 | Smooth |
Coefficient | t-Statistics | p Value | Adjustment to Long-Run Equilibrium | |
---|---|---|---|---|
Constant | −0.0041 | −2. 364135 | 0. 0196 | 0.2629 |
D(Dp) | 0.8244 | 1.817289 | 0.0652 | |
ECM(−1) | −0.2629 | −0.481435 | 0.6450 |
DP | LDR | NPL | NIM | ||
---|---|---|---|---|---|
Mean | 9.2247 | 0.3010 | 0.7948 | 0.0122 | 0.0243 |
Median | 8.8760 | 0.3100 | 0.7432 | 0.0141 | 0.0251 |
Maximum | 16.3015 | 0.6600 | 0.9607 | 0.0173 | 0.0290 |
Minimum | 4.8304 | −0.1100 | 0.6757 | 0.0055 | 0.0192 |
Std. Dev. | 1.9991 | 0.1579 | 0.0944 | 0.0044 | 0.0029 |
Skewness | 0.6383 | −0.1895 | 0.5480 | −0.3365 | −0.1970 |
Kurtosis | 3.8311 | 2.9433 | 1.6649 | 1.4049 | 1.6743 |
Observations | 111 | 111 | 111 | 111 | 111 |
Variable | Test Form | t-Statistics | 10% Threshold | Prob * | Results |
---|---|---|---|---|---|
(c,t,0) | −4.0048 | −4.0436 | 0.0112 | Smooth | |
DP | (c,0,0) | −2.6291 | −2.8879 | 0.0902 | Not smooth |
D(DP) | (c,0,0) | −12.4167 | −2.8882 | 0.0000 | Smooth |
LDR | (c,t,1) | −2.2407 | −3.4516 | 0.4622 | Not smooth |
D(LDR) | (c,t,2) | −7.8801 | −3.4523 | 0.0000 | Smooth |
NPL | (c,0,1) | −1.5993 | −2.8882 | 0.4795 | Not smooth |
D(NPL) | (c,0,0) | −3.2312 | −2.8882 | 0.0208 | Smooth |
NIM | (c,0,1) | −1.7209 | −2.8882 | 0.4180 | Not smooth |
D(NIM) | (c,0,2) | −21.4569 | −2.8887 | 0.0000 | Smooth |
Null Hypothesis | Trace Test | Null Hypothesis | Maximum Eigenvalue Test | ||||
---|---|---|---|---|---|---|---|
Statistics | Threshold (5%) | p Value | Statistics | Threshold (5%) | p Value | ||
None * | 111.2557 | 95.7537 | 0.0028 | None * | 41.3392 | 40.0776 | 0.0358 |
At most 1 * | 69.9165 | 69.8189 | 0.0491 | At most 1 | 25.2442 | 33.8769 | 0.3687 |
At most 2 | 44.6723 | 47.8561 | 0.0966 | At most 2 | 19.2678 | 27.5843 | 0.3941 |
At most 3 | 25.4045 | 29.7971 | 0.1475 | At most 3 | 14.3335 | 21.1316 | 0.3383 |
At most 4 | 11.071 | 15.4947 | 0.2072 | At most 4 | 9.2030 | 14.2646 | 0.2697 |
At most 5 | 1.8680 | 3.8415 | 0.1717 | At most 5 | 1.8680 | 3.8415 | 0.1717 |
Variable | DP | LDR | NPL | NIM |
---|---|---|---|---|
R2 | 0.641 | 0.748 | 0.867 | 0.812 |
VIF | 2.788 | 3.964 | 7.528 | 5.309 |
Variable | Coefficient | Standard Deviation | t-Statistics | p Value |
---|---|---|---|---|
DP | 0.7274 | 2.0160 | 0.3608 | 0.7190 |
LDR | −6.5623 | 4.0210 | −1.6320 | 0.1056 |
NIM | −23.7898 | 152.5949 | −0.1559 | 0.8764 |
NPL | 76.5610 | 118.2888 | 0.6472 | 0.5189 |
ε | 13.8658 | 6.2878 | 2.2052 | 0.0296 |
R-squared | 0.0363 | Mean dependent var | 9.2247 | |
Adjusted R-squared | −0.0001 | S.D. dependent var | 1.9991 | |
S.E. of regression | 1.9992 | Akaike info criterion | 4.2674 | |
Sum squared resid | 423.6586 | Schwarz criterion | 4.3894 | |
Log likelihood | −231.8388 | Hannan-Quinn criter. | 4.3169 | |
F-statistic | 0.9970 | Durbin-Watson stat | 0.5451 | |
Prob(F-statistic) | 0.4126 |
F-Statistics | Prob.F | obs*R-Square | Prob.chi-Square |
---|---|---|---|
3.792163 | 0.0064 | 13.89568 | 0.0076 |
Variable | Coefficient | Standard Deviation | t-Statistics | p Value |
---|---|---|---|---|
ε | 13.3045 | 1.687277 | 7.885192 | 0.0000 |
DP | 0.9027 | 0.417235 | 2.163685 | 0.0327 |
LDR | −5.6124 | 0.680173 | −8.251482 | 0.0000 |
NIM | −23.7731 | 47.19046 | −0.503770 | 0.6155 |
NPL | 55.0378 | 31.47661 | 1.748531 | 0.0833 |
Weighted Statistics | ||||
R-squared | 0.7710 | Mean dependent var | 9.1596 | |
AdjustedR-squared | 0.7623 | S.D. dependent var | 18.6795 | |
S.E. of regression | 0.5254 | Akaike info criterion | 1.5950 | |
Sum squared resid | 29.2712 | Schwarz criterion | 1.7170 | |
Log likelihood | −83.5248 | Hannan-Quinn criter. | 1.6445 | |
F-statistic | 89.2337 | Durbin-Watson stat | 0.9686 | |
Prob(F-statistic) | 0.0000 | Weighted mean dep. | 9.2538 | |
Unweighted Statistics | ||||
R-squared | 0.0355 | Mean dependent var | 9.2247 | |
Adjusted R-squared | −0.0009 | S.D. dependent var | 1.9991 | |
S.E. of regression | 1.9999 | Sum squared resid | 423.9786 | |
Durbin-Watson stat | 0.5442 |
Variable | Mediator Variable | ||
---|---|---|---|
LDR | NPL | NIM | |
DP | 0.8245 *** | 26.9530 *** | −26.6535 *** |
Constant term | −0.3543 *** | −0.0276 | 0.9484 *** |
Impact Path | Direct and Indirect Effect | BootSE | Interval Floor | Interval Ceiling |
---|---|---|---|---|
DP→ | −0.7776 | 0.5005 | −1.7698 | 0.2146 |
DP→LDR→ | −3.1010 | 2.8845 | −9.0448 | 3.2072 |
DP→NPL→ | 5.9292 | 2.0357 | 1.6650 | 10.0711 |
DP→NIM→ | 16.7177 | 7.4589 | 11.3047 | 38.6391 |
Hypothesis | Hypothesis Details | Results |
---|---|---|
H1 | Climate change would reduce the LDR of commercial banks and increase the systemic risk in the banking sector. | Not supported |
H2 | Climate change would increase the NPL ratio of commercial banks and the systemic risk in the banking sector. | Supported |
H3 | Climate change would narrow the NIM of commercial banks and increase the systemic risk in the banking sector. | Not supported |
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Liu, Y.; Huang, C.; Zou, Z.; Chen, Q.; Chu, X. Research into the Mechanism for the Impact of Climate Change on Systemic Risk—A Case Study of China’s Small- and Medium-sized Commercial Banks. Sustainability 2020, 12, 9582. https://doi.org/10.3390/su12229582
Liu Y, Huang C, Zou Z, Chen Q, Chu X. Research into the Mechanism for the Impact of Climate Change on Systemic Risk—A Case Study of China’s Small- and Medium-sized Commercial Banks. Sustainability. 2020; 12(22):9582. https://doi.org/10.3390/su12229582
Chicago/Turabian StyleLiu, Yongping, Chunzhong Huang, Zongbao Zou, Qiao Chen, and Xuan Chu. 2020. "Research into the Mechanism for the Impact of Climate Change on Systemic Risk—A Case Study of China’s Small- and Medium-sized Commercial Banks" Sustainability 12, no. 22: 9582. https://doi.org/10.3390/su12229582