
A Systematic Risk Measure of the Chinese Banking Industry Based on SRISK
Yinjie MA, Muyao LI, Zhiqiang JIANG, Weixing ZHOU
China Journal of Econometrics ›› 2021, Vol. 1 ›› Issue (1) : 114-140.
A Systematic Risk Measure of the Chinese Banking Industry Based on SRISK
Developing effective systematic risk measures plays an important role in preventing systematic risk. Based on detailed information of 16 Chinese commercial banks and daily returns of China Securities Index (CSI) 300 from Jan-2011 to Dec-2019, we estimate the systematic risk measure of SRISK for the Chinese banks, which is the expected shortfall of a bank conditional on a prolonged market decline, by means of Monte Carlo simulations. The SRISK is applied to reveal risk ranking of banks, uncover risk contagion among industries, and predict macroeconomic indicators. Our results highlight that: 1) SRISK delivers robust rankings of systematically important banks; 2) Despite the fact that some commercial banks are in the same category, they have heterogeneous effects on different industries. State-owned banks are contagious to the entire industry; 3) SRISK can be used to predict PPI and RPI in short term. It is also capable to predict CPI in 2~3 months. Our results indicate that SRISK not only can be incorporated into the risk management system to strengthen prudential supervision of the banking industry, but also satisfies the requirements of macroprudential supervision due to its bank-industry sensitivity.
SRISK / DCC-GARCH model / Granger causality test / systemically important banks {{custom_keyword}} /
表1 2011年1月1日前上市的商业银行名单 |
序号 | 股票代码 | 股票名称 | 类别 | 上市时间 |
1 | 000001.SZ | 平安银行 | 股份制商业银行 | 1991-04-03 |
2 | 002142.SZ | 宁波银行 | 城市商业银行 | 2007-07-19 |
3 | 600000.SH | 浦发银行 | 股份制商业银行 | 1999-11-10 |
4 | 600015.SH | 华夏银行 | 股份制商业银行 | 2003-09-12 |
5 | 600016.SH | 民生银行 | 股份制商业银行 | 2000-12-19 |
6 | 600036.SH | 招商银行 | 股份制商业银行 | 2002-04-09 |
7 | 601009.SH | 南京银行 | 城市商业银行 | 2007-07-19 |
8 | 601166.SH | 兴业银行 | 股份制商业银行 | 2007-02-05 |
9 | 601169.SH | 北京银行 | 城市商业银行 | 2007-09-19 |
10 | 601288.SH | 农业银行 | 国有商业银行 | 2010-07-15 |
11 | 601328.SH | 交通银行 | 国有商业银行 | 2007-05-15 |
12 | 601398.SH | 工商银行 | 国有商业银行 | 2006-10-27 |
13 | 601818.SH | 光大银行 | 股份制商业银行 | 2010-08-18 |
14 | 601939.SH | 建设银行 | 国有商业银行 | 2007-09-25 |
15 | 601988.SH | 中国银行 | 国有商业银行 | 2006-07-05 |
16 | 601998.SH | 中信银行 | 股份制商业银行 | 2007-04-27 |
表2 市场及商业银行日度收益率的描述性统计 |
机构名称 | 平均数 | 最大值 | 最小值 | 标准差 | ADF |
市场指数 | 0.0001232 | 0.0649887 | –0.0915443 | 0.0144848 | –45.6377*** |
平安银行 | 0.0005197 | 0.0955476 | –0.1055774 | 0.0210058 | –46.4299*** |
宁波银行 | 0.0003171 | 0.0959727 | –0.1055361 | 0.0239517 | –47.6768*** |
浦发银行 | 0.0003630 | 0.2006448 | –0.1056508 | 0.0220494 | –46.6589*** |
华夏银行 | 0.0003900 | 0.0959486 | –0.1058943 | 0.0230334 | –47.1864*** |
民生银行 | 0.0004703 | 0.0962285 | –0.1053612 | 0.0210747 | –46.7781*** |
招商银行 | 0.0006616 | 0.0956142 | –0.1054425 | 0.0214779 | –47.6227*** |
南京银行 | 0.0002836 | 0.0959128 | –0.1055123 | 0.0221645 | –45.6588*** |
兴业银行 | 0.0004151 | 0.0957906 | –0.1055822 | 0.0237139 | –46.6738*** |
北京银行 | –0.0000518 | 0.0958001 | –0.1055075 | 0.0208227 | –48.8540*** |
农业银行 | 0.0003421 | 0.0964179 | –0.1042379 | 0.0136319 | –46.8061*** |
交通银行 | –0.0000859 | 0.0962575 | –0.1059963 | 0.0198264 | –44.8609*** |
工商银行 | 0.0003611 | 0.0958075 | –0.1053520 | 0.0171654 | –47.4571*** |
光大银行 | 0.0002383 | 0.0966340 | –0.1044459 | 0.0176639 | –45.7254*** |
建设银行 | 0.0001415 | 0.0956573 | –0.1063889 | 0.0177650 | –46.7705*** |
中国银行 | 0.0001719 | 0.0967912 | –0.1058078 | 0.0169363 | –46.2804*** |
中信银行 | –0.0000621 | 0.0961287 | –0.1056430 | 0.0225084 | –44.3290*** |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
表3 各银行收益率的GJR-GARCH模型参数估计 |
平安银行 | 0.04520*** | –0.01806* | 0.96275*** |
(4.90245) | (–1.88364) | (92.14289) | |
宁波银行 | 0.04812*** | 0.011416 | 0.93439*** |
(2.67157) | (0.59125) | (33.96591) | |
浦发银行 | 0.02444*** | 0.008144 | 0.969813*** |
(2.38290) | (0.77565) | (119.78238) | |
华夏银行 | 0.07920*** | –0.02807** | 0.932189*** |
(3.78606) | (–1.97771) | (48.36871) | |
民生银行 | 0.08923*** | –0.02772* | 0.92333*** |
(4.49070) | (–1.82780) | (54.97935) | |
招商银行 | 0.03691*** | –0.0074 | 0.962502*** |
(5.41905) | (–0.63538) | (101.38266) | |
南京银行 | 0.06181*** | 0.004764 | 0.928186*** |
(4.10826) | (0.50702) | (53.44626) | |
兴业银行 | 0.05611*** | –0.0038 | 0.944927*** |
(4.53548) | (–0.68886) | (82.44751) | |
北京银行 | 0.04844*** | –0.00888 | 0.953532*** |
(3.53269) | (–0.81552) | (74.32663) | |
农业银行 | 0.069562 | –0.03421** | 0.936797*** |
(1.45572) | (–2.02405) | (15.97875) | |
交通银行 | 0.12104*** | –0.04575 | 0.898124*** |
(3.00560) | (–1.64235) | (33.08305) | |
工商银行 | 0.09554*** | –0.03538*** | 0.921244*** |
(3.84687) | (–2.33130) | (42.42581) | |
光大银行 | 0.13906*** | –0.06736** | 0.879513*** |
(3.20155) | (–2.22310) | (25.21735) | |
建设银行 | 0.09669*** | 0.002042 | 0.893728*** |
(4.20896) | (1.23825) | (37.84939) | |
中国银行 | 0.10505*** | –0.03641* | 0.909262*** |
(3.53243) | (–1.83943) | (36.43817) | |
中信银行 | 0.09592*** | –0.02554 | 0.905249*** |
(3.19739) | (–1.34776) | (31.41630) |
注: 样本期为2011年1月1日至2019年11月30日.其中, *、**、*** 分别表示10%、5%和1%的显著性水平. |
表4 各银行与市场的DCC-GARCH模型参数估计 |
平安银行 | 0.0621*** | 0.9199*** | 0.9821 |
(3.4038) | (48.0811) | ||
宁波银行 | 0.0249** | 0.9613*** | 0.9862 |
(1.9788) | (106.1528) | ||
浦发银行 | 0.0410*** | 0.9472** | 0.9882 |
(3.4729) | (56.5071) | ||
华夏银行 | 0.0167* | 0.9620*** | 0.9787 |
(1.6876) | (69.3812) | ||
民生银行 | 0.0431 | 0.9405*** | 0.9836 |
(1.6353) | (31.9185) | ||
招商银行 | 0.0330** | 0.9553*** | 0.9884 |
(2.0606) | (82.1928) | ||
南京银行 | 0.0235 | 0.9421*** | 0.9707 |
(1.0762) | (32.9712) | ||
兴业银行 | 0.0399*** | 0.9421*** | 0.9821 |
(2.5037) | (46.3023) | ||
北京银行 | 0.0361 | 0.9453*** | 0.9782 |
(0.2040) | (8.3816) | ||
农业银行 | 0.0204* | 0.9364 | 0.9657 |
(1.6568) | (56.8831) | ||
交通银行 | 0.0407*** | 0.9561*** | 0.9771 |
(2.7861) | (47.7252) | ||
工商银行 | 0.0284 | 0.9494*** | 0.9844 |
(0.7442) | (2.9224) | ||
光大银行 | 0.0256 | 0.9596*** | 0.9750 |
(1.1367) | (26.003) | ||
建设银行 | 0.0258*** | 0.9593*** | 0.9854 |
(2.7700) | (96.2202) | ||
中国银行 | 0.0289 | 0.9488*** | 0.9778 |
(0.8256) | (23.6748) | ||
中信银行 | 0.0331*** | 0.9467*** | 0.9798 |
(2.3373) | (58.4865) |
注: 样本期为2011年1月1日至2019年11月30日.其中, *、**、*** 分别表示10%、5%和1%的显著性水平. |
表5 商业银行SRISK指标的描述性统计 |
机构名称 | 平均数 | 中位数 | 最大值 | 最小值 | 标准差 |
平安银行 | 651.29 | 676.48 | 1246.64 | 105.69 | 273.45 |
宁波银行 | 62.36 | 95.89 | 238.05 | –207.33 | 102.75 |
浦发银行 | 1321.39 | 1343.32 | 2273.58 | 116.50 | 518.50 |
华夏银行 | 717.66 | 729.07 | 1252.98 | 181.02 | 276.78 |
民生银行 | 1150.99 | 944.13 | 2497.79 | –8.87 | 765.10 |
招商银行 | 11.29 | 284.93 | 1688.74 | –2305.99 | 964.39 |
南京银行 | 200.70 | 189.02 | 480.66 | –91.50 | 154.26 |
兴业银行 | 1437.05 | 1495.31 | 2342.83 | 77.21 | 581.82 |
北京银行 | 440.21 | 401.40 | 1046.87 | –66.62 | 262.25 |
农业银行 | 4517.22 | 5003.63 | 8086.72 | 401.73 | 1886.90 |
交通银行 | 2080.74 | 2061.66 | 3899.74 | 168.14 | 983.97 |
工商银行 | 2622.62 | 3335.04 | 5602.54 | –2466.87 | 2068.29 |
光大银行 | 1021.56 | 954.33 | 1827.58 | 24.87 | 487.57 |
建设银行 | 1988.86 | 2327.04 | 4430.25 | –2407.04 | 1846.86 |
中国银行 | 4044.33 | 4376.77 | 7621.59 | 175.60 | 1864.38 |
中信银行 | 1295.85 | 1407.91 | 2697.81 | –238.79 | 737.14 |
注: 单位为亿元. |
表6 2019年12月商业银行各指标排名及G-SIB评价 |
机构名称 | SRISK排名 | LRMES排名 | 市值排名 | G-SIB评级 |
中国银行 | 1 | 16 | 4 | |
农业银行 | 2 | 11 | 3 | |
工商银行 | 3 | 9 | 1 | |
交通银行 | 4 | 14 | 6 | 未入列 |
建设银行 | 5 | 10 | 2 | |
中信银行 | 6 | 7 | 10 | 未入列 |
民生银行 | 7 | 15 | 11 | 未入列 |
浦发银行 | 8 | 6 | 8 | 未入列 |
兴业银行 | 9 | 3 | 7 | 未入列 |
光大银行 | 10 | 4 | 12 | 未入列 |
华夏银行 | 11 | 12 | 15 | 未入列 |
北京银行 | 12 | 13 | 14 | 未入列 |
平安银行 | 13 | 2 | 9 | 未入列 |
南京银行 | 14 | 8 | 16 | 未入列 |
宁波银行 | 15 | 1 | 13 | 未入列 |
招商银行 | 16 | 5 | 5 | 未入列 |
表7 国有银行SRISK增长率格兰杰因果检验结果 |
Null Hypothesis | Prob. | |
SRISK% | 0.55765 | 0.5743 |
SRISK% | 1.80450 | 0.1698 |
SRISK% | 0.94218 | 0.3932 |
SRISK% | 0.24849 | 0.7805 |
SRISK% | 8.67455 | 0.0003 |
SRISK% | 11.9175 | 2E |
SRISK% | 9.35179 | 0.0002 |
SRISK% | 6.51352 | 0.0022 |
SRISK% | 0.10564 | 0.8998 |
SRISK% | 0.06429 | 0.9378 |
SRISK% | 0.96251 | 0.3854 |
SRISK% | 0.41875 | 0.6590 |
SRISK% | 0.81944 | 0.4436 |
SRISK% | 3.60105 | 0.0309 |
SRISK% | 0.38997 | 0.6781 |
SRISK% | 1.27040 | 0.2852 |
SRISK% | 4.81409 | 0.0101 |
SRISK% | 0.61086 | 0.5449 |
SRISK% | 5.27274 | 0.0066 |
SRISK% | 6.84076 | 0.0016 |
表8.1 SRISK对PPI的回归结果(1~6期) |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.000 | 0.002 | –0.001 | 0.004 | 0.002 | 0.003 | |
–0.009 | –0.009 | –0.016 | 0.003 | 0.011 | 0.012 | |
–0.031** | –0.018 | –0.007 | 0.008 | 0.009 | 0.022* | |
–0.022** | –0.006 | –0.004 | 0.008 | 0.018* | 0.022** | |
0.251*** | 0.011 | –0.172* | –0.103 | –0.040 | –0.091 | |
0.004 | –0.109 | –0.035 | –0.027 | –0.112 | –0.006 | |
–0.161* | –0.055 | –0.103 | –0.092 | 0.019 | –0.006 | |
–0.023*** | –0.006 | –0.007 | 0.000 | 0.000 | 0.004 | |
–0.070 | –0.130 | 0.081 | 0.006 | 0.088 | 0.122 | |
0.017 | –0.004 | 0.042 | –0.044 | –0.046 | –0.064* | |
0.012 | 0.010 | 0.013 | –0.012 | –0.009 | –0.014 | |
0.192 | 0.069 | 0.079 | 0.061 | 0.089 | 0.139 |
表8.2 SRISK对PPI的回归结果(7~12期) |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.005 | 0.004 | 0.003 | 0.002 | 0.005 | 0.007 | |
0.009 | 0.017 | 0.019 | 0.008 | 0.009 | 0.018 | |
0.015 | 0.025* | 0.010 | 0.003 | 0.021 | 0.019 | |
0.023* | 0.016 | 0.010 | 0.011 | 0.011 | 0.009 | |
RPI | 0.057 | 0.171 | 0.247 | 0.418 | 0.345 | 0.312** |
RPI | 0.084 | 0.106 | 0.227 | –0.010 | 0.040 | –0.036 |
RPI | 0.211 | 0.121 | –0.140 | –0.181 | –0.316 | –0.352 |
0.002 | 0.002 | –0.004 | –0.009 | 0.003 | 0.010 | |
–0.097 | –0.112 | –0.070 | 0.029 | 0.020 | 0.114 | |
–0.037 | –0.037 | –0.043 | 0.011 | –0.022 | –0.082 | |
–0.031 | –0.023 | –0.021 | –0.024 | –0.042 | –0.056 | |
0.179 | 0.192 | 0.180 | 0.168 | 0.150 | 0.191 |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
表9.1 SRISK对RPI的回归结果(1~6期) |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.000 | 0.001 | 0.001 | 0.001 | 0.003 | 0.001 | |
–0.001 | 0.005 | 0.014* | 0.010 | 0.002 | –0.003 | |
0.007 | 0.017** | 0.016* | –0.008 | –0.014 | –0.002 | |
0.012** | 0.018** | 0.009 | –0.009 | –0.006 | 0.005 | |
1.064*** | 0.763*** | 0.472*** | 0.272 | 0.095 | 0.035 | |
–0.401*** | –0.361* | –0.277 | –0.289 | –0.160 | –0.142 | |
0.069 | 0.091 | 0.127 | 0.237 | 0.290 | 0.408** | |
0.004 | 0.002 | –0.005 | –0.015 | –0.008 | 0.001 | |
–0.039 | –0.130 | –0.217* | –0.197* | –0.207* | –0.148 | |
0.011 | –0.006 | 0.013 | 0.013 | –0.040 | –0.018 | |
0.000 | 0.001 | 0.001 | –0.002 | –0.007 | –0.003 | |
0.689 | 0.317 | 0.149 | 0.095 | 0.084 | 0.111 |
表9.2 SRISK对RPI的回归结果(7~12期) |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.001 | 0.000 | –0.001 | –0.002 | 0.000 | 0.002 | |
0.006 | 0.014 | 0.019 | 0.008 | 0.008 | 0.016 | |
0.010 | 0.018* | 0.007 | 0.000 | 0.018 | 0.023 | |
0.021* | 0.014 | 0.008 | 0.008 | 0.011 | 0.010 | |
0.011 | 0.105 | 0.252 | 0.510*** | 0.488*** | 0.365** | |
0.005 | 0.149 | 0.302 | 0.019 | –0.079 | –0.190 | |
0.384** | 0.217 | –0.136 | –0.248 | –0.324* | –0.268 | |
–0.001 | –0.005 | –0.010 | –0.013 | 0.001 | 0.012 | |
–0.140 | –0.156 | –0.108 | 0.000 | –0.033 | 0.009 | |
–0.006 | –0.004 | –0.012 | 0.037 | 0.014 | –0.032 | |
–0.004 | 0.004 | 0.005 | 0.003 | –0.009 | –0.015 | |
0.182 | 0.218 | 0.219 | 0.215 | 0.158 | 0.117 |
注: *、**、***分别表示10%、5%和1%的显著性水平. |
表10.1 SRISK对CPI的回归结果(1~6期) |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.002 | 0.002 | –0.001 | 0.005* | 0.003 | 0.004 | |
–0.005 | –0.010 | –0.022* | 0.003 | 0.009 | 0.015 | |
–0.037*** | –0.018 | –0.006 | 0.007 | 0.016 | 0.028* | |
–0.028** | –0.005 | –0.008 | 0.010 | 0.027** | 0.019 | |
0.137 | –0.049 | –0.265*** | –0.137 | 0.021 | –0.025 | |
0.014 | –0.223** | –0.099 | 0.003 | –0.051 | 0.089 | |
–0.262*** | –0.089 | –0.027 | –0.001 | 0.123 | –0.036 | |
–0.028*** | –0.002 | –0.004 | 0.004 | 0.004 | 0.003 | |
–0.135 | –0.104 | 0.144 | 0.019 | 0.127 | 0.116 | |
–0.013 | –0.022 | 0.026 | –0.063 | –0.030 | –0.071* | |
0.015 | 0.015 | 0.017 | –0.013 | –0.013 | –0.018 | |
0.208 | 0.108 | 0.128 | 0.051 | 0.088 | 0.130 |
表10.2 SRISK对CPI的回归结果(7~12期) |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.002 | 0.002 | –0.001 | 0.005* | 0.003*** | 0.004 | |
–0.005 | –0.010 | –0.022 | 0.003 | 0.009 | 0.015 | |
–0.037 | –0.018* | –0.006 | 0.007 | 0.016 | 0.028 | |
–0.028 | –0.005* | –0.008 | 0.010 | 0.027 | 0.019 | |
0.137 | –0.049 | –0.265 | –0.137* | 0.021** | –0.025*** | |
0.014 | –0.223 | –0.099*** | 0.003 | –0.051*** | 0.089*** | |
–0.262 | –0.089* | –0.027** | –0.001*** | 0.123*** | –0.036 | |
–0.028 | –0.002 | –0.004** | 0.004 | 0.004 | 0.003 | |
–0.135 | –0.104 | 0.144** | 0.019 | 0.127 | 0.116 | |
–0.013 | –0.022 | 0.026 | –0.063 | –0.030 | –0.071* | |
0.015 | 0.015 | 0.017 | –0.013* | –0.013*** | –0.018 | |
0.208 | 0.108 | 0.128 | 0.051 | 0.088 | 0.130 |
注: *、**、***分别表示10%、5%和1%的显著性水平. |
表11 不同参数下SRISK排名的Spearman相关系数 |
2013-Q1 | 0.9735*** | 0.8029*** | 0.8029*** |
2014-Q1 | 0.9882*** | 0.9676*** | 0.9618*** |
2015-Q1 | 0.9882*** | 0.9618*** | 0.9618*** |
2016-Q1 | 0.9941*** | 0.9765*** | 0.9765*** |
2017-Q1 | 0.9971*** | 0.9794*** | 0.9735*** |
2018-Q1 | 0.9971*** | 0.9794*** | 0.9735*** |
2019-Q1 | 0.9912*** | 0.9676*** | 0.9618*** |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
表12.1 SRISK |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.001 | 0.002 | 0.000 | 0.004 | 0.002 | 0.003 | |
0.002 | 0.000 | –0.003 | –0.005 | –0.005 | 0.007* | |
0.000* | 0.000 | –0.006 | –0.006* | 0.004 | 0.004 | |
0.002* | –0.005 | –0.007 | 0.002 | 0.001 | 0.007* | |
0.266*** | 0.020 | –0.152 | –0.109 | –0.069 | –0.115 | |
–0.029 | –0.121 | –0.040 | –0.018 | –0.097 | 0.023 | |
–0.133 | –0.045 | –0.092 | –0.098 | 0.001 | –0.026 | |
–0.009 | 0.001 | –0.006 | –0.006 | –0.003 | –0.005 | |
–0.073 | –0.139 | 0.047 | 0.001 | 0.122 | 0.119 | |
0.012 | –0.004 | 0.036 | –0.046 | –0.034 | –0.062* | |
0.009 | 0.009 | 0.010 | –0.012 | –0.004 | –0.011 | |
0.127 | 0.072 | 0.108 | 0.118 | 0.096 | 0.138 |
表12.2 SRISK |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.001 | 0.002 | 0.001 | 0.004 | 0.007*** | 0.003 | |
0.004 | 0.004 | 0.002 | –0.004 | –0.005 | –0.003 | |
0.004 | 0.003 | 0.001 | –0.003 | –0.006* | –0.004 | |
0.005 | 0.001 | –0.001 | –0.005 | –0.006* | 0.000 | |
–0.023 | –0.006 | –0.008 | –0.063 | 0.155* | 0.371 | |
–0.003 | –0.017 | –0.157 | 0.127 | 0.376*** | 0.248 | |
–0.045 | –0.033 | 0.261*** | 0.403*** | 0.223*** | –0.137 | |
–0.002 | –0.003 | 0.018*** | 0.009 | –0.003 | –0.002 | |
0.119 | 0.023 | 0.136 | –0.001 | –0.131* | –0.013 | |
–0.030 | –0.008 | 0.006 | –0.012 | –0.039 | 0.039 | |
–0.005 | –0.006 | –0.006 | –0.023 | –0.044*** | –0.022 | |
0.067 | 0.024 | 0.176 | 0.283 | 0.363 | 0.314 |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
表13.1 SRISK$ |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.000 | 0.001 | 0.001 | 0.001 | 0.003 | 0.001 | |
0.003 | 0.003 | –0.001 | 0.001 | 0.002 | 0.001 | |
–0.001 | 0.004* | –0.002 | –0.002 | 0.000 | 0.003 | |
0.000 | 0.001* | 0.000 | 0.002 | 0.004 | 0.001 | |
1.078*** | 0.784*** | 0.459*** | 0.268 | 0.100 | 0.031 | |
–0.421*** | –0.410* | –0.289 | –0.251 | –0.141 | –0.142 | |
0.068 | 0.108 | 0.136 | 0.210 | 0.282 | 0.414** | |
0.000 | –0.007 | –0.012 | –0.013 | –0.003 | 0.002 | |
–0.056 | –0.147 | –0.210* | –0.196* | –0.207* | –0.145 | |
0.009 | –0.008 | 0.015 | 0.008 | –0.044 | –0.015 | |
0.001 | 0.002 | 0.003 | –0.003 | –0.008 | –0.003 | |
0.684 | 0.307 | 0.137 | 0.084 | 0.083 | 0.112 |
表13.2 SRISK |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.001 | –0.001 | –0.001 | –0.002 | 0.000 | 0.001 | |
0.003 | 0.001 | 0.004 | 0.005 | 0.000 | –0.003 | |
–0.001 | 0.000 | 0.003 | –0.001 | –0.004 | –0.008* | |
0.003 | 0.004 | 0.001 | –0.001 | –0.007 | –0.006 | |
0.027 | 0.085 | 0.208 | 0.511*** | 0.488*** | 0.362** | |
–0.042 | 0.140 | 0.378 | 0.051 | –0.100 | –0.237 | |
0.409** | 0.237 | –0.178 | –0.287* | –0.328* | –0.247 | |
–0.006 | –0.013* | –0.012 | –0.014* | –0.008 | –0.001 | |
–0.151 | –0.146 | –0.096 | –0.012 | –0.042 | 0.004 | |
–0.005 | 0.001 | –0.009 | 0.034 | 0.016 | –0.029 | |
–0.001 | 0.007 | 0.007 | 0.003 | –0.007 | –0.011 | |
0.167 | 0.205 | 0.206 | 0.222 | 0.174 | 0.132 |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
表14.1 SRISK |
领先期 | 1 | 2 | 3 | 4 | 5 | 6 |
0.001 | 0.001 | –0.001 | 0.004 | 0.001 | 0.003 | |
0.004 | 0.000 | –0.004 | –0.013** | 0.000 | 0.004 | |
0.002 | –0.001* | –0.012** | –0.006 | 0.003 | 0.007 | |
0.003 | –0.009* | –0.006 | –0.002 | 0.004 | 0.008 | |
0.080 | –0.093 | –0.254*** | –0.127 | 0.009 | –0.025 | |
–0.064 | –0.239** | –0.092 | 0.013 | –0.013 | 0.099 | |
–0.231** | –0.088 | –0.034 | –0.030 | 0.101 | –0.069 | |
–0.011 | 0.006 | –0.005 | –0.002 | –0.002 | –0.005 | |
–0.113 | –0.087 | 0.095 | 0.017 | 0.107 | 0.118 | |
–0.017 | –0.015 | 0.027 | –0.051 | –0.019 | –0.061 | |
0.022 | 0.027 | 0.024 | –0.005 | –0.003 | –0.008 | |
0.138 | 0.152 | 0.163 | 0.106 | 0.040 | 0.114 |
表14.2 SRISK |
领先期 | 7 | 8 | 9 | 10 | 11 | 12 |
0.000 | 0.000 | –0.002 | 0.004 | 0.007*** | 0.000 | |
0.007 | 0.007 | 0.002 | –0.003 | –0.012*** | 0.002 | |
0.010** | 0.007 | 0.000 | –0.013*** | –0.004 | 0.002 | |
0.009* | 0.002 | –0.012*** | –0.004 | –0.003 | 0.003 | |
0.064 | –0.092 | –0.137 | –0.125 | 0.180** | 0.501*** | |
–0.089 | –0.148 | –0.229** | 0.162 * | 0.518*** | 0.238*** | |
–0.140 | –0.136 | 0.200** | 0.488*** | 0.225*** | –0.160* | |
0.000 | –0.005 | 0.021*** | 0.003 | –0.004 | 0.000 | |
0.078 | 0.032 | 0.226** | –0.013 | –0.055 | –0.030 | |
–0.015 | 0.013 | 0.029 | –0.009 | –0.028 | 0.089** | |
0.012 | 0.016 | 0.012 | –0.022 | –0.049*** | –0.008 | |
0.121 | 0.108 | 0.304 | 0.421 | 0.469 | 0.422 |
注: *、**、*** 分别表示10%、5%和1%的显著性水平. |
陈湘鹏, 周皓, 金涛, 王正位, 微观层面系统性金融风险指标的比较与适用性分析——基于中国金融系统的研究[J]. 金融研究, 2019, 467 (5): 17- 36.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
刚健华, 赵扬, 高翔, 短期跨境资本流动, 金融市场与系统性风险[J]. 经济理论与经济管理, 2018, (4): 98- 112.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
李红权, 何敏园, 黄莹莹, 我国金融机构的系统重要性评估: 基于多元极值理论[J]. 中国管理科学, 2020, 28 (5): 14- 24.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
梁琪, 李政, 郝项超, 我国系统重要性金融机构的识别与监管——基于系统性风险指数SRISK方法的分析[J]. 金融研究, 2013, (9): 56- 70.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
毛泽盛, 王元, 中国信贷波动对金融系统性风险影响的实证研究[J]. 国际金融研究, 2015, 339 (12): 25- 33.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
史永东, 王龑, 宋西伟, 中国金融机构系统风险对宏观经济的影响[J]. 投资研究, 2017, 36 (12): 65- 77.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
隋聪, 迟国泰, 王宗尧, 网络结构与银行系统性风险[J]. 管理科学学报, 2014, 17 (4): 57- 70.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
唐振鹏, 谢智超, 冉梦, 陈菊琴, 网络视角下我国上市银行间市场系统性风险实证研究[J]. 中国管理科学, 2016, 24 (S1): 489- 494.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
童中文, 魏歆七, 中国金融机构系统重要性的度量[J]. 统计与决策, 2017, (8): 146- 149.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
王广龙, 熊利平, 王连猛, SRISK系统性风险测算方法、结果及评述[J]. 投资研究, 2014, 33 (4): 63- 73.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
王周伟, 万里欢, 茆训诚, 中国系统重要性银行的市场识别法有效性研究[J]. 金融理论与实践, 2015, (4): 53- 59.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
张琳, 汤薇, 林晓婕, 周媛, 基于SVM-SRISK的非上市保险公司系统性风险度量[J]. 保险研究, 2018, (6): 3- 15.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
周强, 杨柳勇, 论中国系统重要性银行识别——市场模型法还是指标法[J]. 国际金融研究, 2014, (9): 70- 79.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
朱晓谦, 李靖宇, 李建平, 陈懿冰, 魏璐, 基于危机条件概率的系统性风险度量研究[J]. 中国管理科学, 2018, 26 (6): 1- 7.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Acharya V V, Steffen S, (2014). Falling Short of Expectations? Stress-testing the European Banking System[EB/OL]. https://ideas.repec.org/p/eps/cepswp/8803.html.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Black F, (1976). Studies of Stock Price Volatility Changes[C]//Proceedings of the Business and Economic Statistics Section: 177-181.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Brownlees C T, Engle R, (2011). Volatility, Correlation and Tails for Systemic Risk Measurement[EB/OL]. http://citeseerx.ist.psu.edu/viewdoc/summary? doi: 10.1.1.463.2902.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Engle R F, Ruan T, (2018). How Much SRISK is Too Much[EB/OL]. https://dx.doi.org/10.2139/ssrn.3108269.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Grinderslev O J, Kristiansen K L, (2016). Systemic Risk in Danish Banks: Implementing SRISK in a Danish Context[R]. Danmarks Nationalbank Working Papers.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Tavolaro S, Visnovsky F, (2014). What is the Information Content of the Srisk Measure as a Supervisory Tool[EB/OL]. EconPapers. https://EconPapers.repec.org/RePEc:bfr:decfin:10.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
/
〈 |
|
〉 |