基于知识图谱网络特征的中国外汇市场系统性风险测度研究

doi:10.16381/j.cnki.issn1003-207x.2024.1799

Abstract

Abstract:

With the advancement of economic globalization and financial integration， the risk correlation between the global foreign exchange market and China’s foreign exchange market has become more complex， and the importance of the foreign exchange market to China’s economic development， financial security and opening up has become increasingly prominent. In recent years， major events at home and abroad， such as China-Us trade frictions， the COVID-19 epidemic， the US Federal Reserve’s intensive interest rate hike， and the Russia-Ukraine conflict， have further exacerbated the spread of systemic risks around the world. In order to ensure the healthy operation of the financial market and the stable development of the foreign exchange market， it is necessary to effectively monitor the level of systemic risk in the foreign exchange market. Therefore， accurately measuring the systemic risk of the foreign exchange market has important research value.There are abundant researches on the measurement of systemic risk， but the measurement of systemic risk in China’s foreign exchange market is still relatively blank. Since there is no authoritative and unified definition of systemic risk， based on the existing definition and the characteristics of systemic risk， the systemic risk of foreign exchange market is defined as the sum of the individual risk and the contagion risk of the participants in the foreign exchange market. Under this definition， in order to measure the systemic risk of China’s foreign exchange market， Chinese foreign exchange market-making banks are selected as the main participants in China’s foreign exchange market， the important role of such players in the formation of systemic risk in China’s foreign exchange market is analyzed， and the formation mechanism of systemic risk in China’s foreign exchange market is summarized based on this. And then the systemic risk level of China’s foreign exchange market is effectively measured.Three channels of impact of external events on the foreign exchange market are put forward， with Chinese foreign exchange market makers as the core. Based on the three channels， three measurement mechanisms are put forward from the perspectives of "top-down"， "bottom-up" and "whether to consider scale and leverage"， and then three corresponding risk measurement methods are chosen： ΔCoVaR， MES and SRISK. At the same time， in order to take into account the increasingly important relevance dimension， the knowledge graph of China’s foreign exchange market is constructed， and the weighted degree centrality is constructed as the characteristic index of the knowledge graph network. Combining three types of risk measurement methods and network characteristics， three types of systematic risk measurement indicators of China’s foreign exchange market are proposed： KN-ΔCoVaR， KN-MES and KN-SRISK. In order to verify the validity of the measurement index of this paper， 13 major events at home and abroad are selected as verification， which are divided into long events and short events， and the effectiveness of the measurement index is illustrated by analyzing the sensitivity of the measurement index to the events.The weekly data from 2016 to 2022 are selected， including the central parity data of 23 currencies， the yield data of 18 foreign exchange market makers， and the data of 3 RMB exchange rate indices （CFETS， SDR， BIS）. The validity of systemic risk indicators in the foreign exchange market is verified by empirical evidence. The indicators and indicators under the characteristics of knowledge graph network can effectively measure the short-term and long-term systemic risk level of China’s foreign exchange market under shocks. The use of CFETS as the index of China’s foreign exchange market is more conducive to measuring the systemic risk of China’s foreign exchange market. On this basis， the systematic importance ranking of China’s foreign exchange market makers banks is analyzed， and it is found that in the long run， the four major state-owned banks and Bank of Communications are more important， while in the short run， local banks are more important. Through the simulation of event shocks， the changes in the ranking of the systemic importance of foreign exchange market makers under different impact intensities of the three types of shock channels are analyzed， and the rules are summarized and found as follows： Under the influence of long-term events， it is necessary to strengthen the supervision of high systemically important banks （such as CITB）， taking into account the supervision cost， the supervision level of low systemically important banks （such as CMSB） can be appropriately reduced.The proposed systematic risk measurement index of China’s foreign exchange market can effectively reflect the impact of major events and provide effective monitoring tools for foreign exchange market regulators. In this paper， the ranking of systemically important forex market makers and their changing characteristics are presented to provide decision-making suggestions for forex market supervision departments.

Key words: knowledge graph network characteristic, China’s foreign exchange market, systemic risk, foreign exchange market makers, systemic importance

CLC Number:

F832.5

Jiaxian Shen, Haozhi Chen, Weiguo Zhang. Research on Systemic Risk Measurement of China’ s Foreign Exchange Market Based on Knowledge Graph Network Characteristics[J]. Chinese Journal of Management Science, 2025, 33(3): 45-61.

Figures/Tables 17

市场指数	年份	$K N - Δ C o V a R ¯ - Q$	$K N - Δ C o V a R ¯ - D C C$	$K N - M E S ¯$	$K N - S R I S K ¯$
CFETS	2016	0.000754	0.000184	0.004155	739332
	2017	0.000750	0.000187	0.003792	703133
	2018	0.000876	0.000204	0.004971	741443
	2019	0.000764	0.000243	0.004721	857065
	2020	0.000742	0.000192	0.003960	1084563
	2021	0.000725	0.000208	0.004155	1252697
	2022	0.000719	0.000220	0.003793	1529392
BIS	2016	0.000465	0.000015	0.002154	708820
	2017	0.000532	0.000020	0.001902	671060
	2018	0.000665	0.000023	0.002930	693819
	2019	0.000536	0.000039	0.003247	831891
	2020	0.000523	0.000027	0.002556	1060999
	2021	0.000535	0.000030	0.002839	1232814
	2022	0.000500	0.000032	0.002252	1508080
SDR	2016	0.001514	0.000684	0.006383	769545
	2017	0.001515	0.000610	0.005948	737166
	2018	0.001779	0.000733	0.007331	785609
	2019	0.001544	0.000893	0.007771	898385
	2020	0.001490	0.000750	0.006775	1290434
	2021	0.001482	0.000690	0.006656	1283874
	2022	0.001460	0.000725	0.005714	1558482

人民币汇率指数	$i$	$j$	-0.1水平点数	0.4水平点数
CFETS	$Δ C o V a R - Q$	$Δ C o V a R - D C C$	7	3
	$Δ C o V a R - Q$	$M E S$	2	3
	$M E S$	$S R I S K$	3	2
	$K N - Δ C o V a R - Q$	$K N - Δ C o V a R - D C C$	7	4
	$K N - Δ C o V a R - Q$	$K N - M E S$	0	5
	$K N - M E S$	$K N - S R I S K$	4	2
SDR	$Δ C o V a R - Q$	$Δ C o V a R - D C C$	6	4
	$Δ C o V a R - Q$	$M E S$	1	5
	$M E S$	$S R I S K$	1	2
	$K N - Δ C o V a R - Q$	$K N - Δ C o V a R - D C C$	8	3
	$K N - Δ C o V a R - Q$	$K N - M E S$	2	4
	$K N - M E S$	$K N - S R I S K$	3	1
BIS	$Δ C o V a R - Q$	$Δ C o V a R - D C C$	4	5
	$Δ C o V a R - D C C$	$M E S$	1	3
	$M E S$	$S R I S K$	5	0
	$K N - Δ C o V a R - Q$	$K N - Δ C o V a R - D C C$	4	8
	$K N - Δ C o V a R - D C C$	$K N - M E S$	1	4
	$K N - M E S$	$K N - S R I S K$	6	0

指标	年度	1	2	3	4	5	6	7	8	9	10
$K N - M E S$	2016	NJCB	BCHO	NBCB	CMHO	BCOH	CCBH	DESZ	EBBC	HXBJ	ICBC
	2017	NJCB	BKSH	BCHO	NBCB	BCOH	EBBC	CMHO	DESZ	CCBH	HXBJ
	2018	NJCB	BKSH	BCHO	NBCB	CCBH	CMHO	EBBC	BCOH	ICBC	HXBJ
	2019	NJCB	QDRC	BKSH	BCHO	NBCB	CMHO	DESZ	EBBC	BCOH	HXBJ
	2020	NJCB	BKSH	QDRC	BCHO	NBCB	DESZ	CMHO	EBBC	BCOH	CCBH
	2021	NJCB	BKSH	NBCB	BCHO	DESZ	CMHO	QDRC	BCOH	EBBC	CCBH
	2022	NJCB	BKSH	NBCB	BCHO	DESZ	QDRC	CMHO	BCOH	EBBC	HXBJ
$K N - S R I S K$	2016	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	CTIB	CMSB	SPDB	EBBC
	2017	ABCI	ICBC	BCHO	CCBH	BCOH	CMSB	IBCN	CTIB	SPDB	EBBC
	2018	ABCI	ICBC	BCHO	CCBH	BCOH	IBCN	CMSB	SPDB	CTIB	EBBC
	2019	ABCI	ICBC	BCHO	CCBH	BCOH	CMSB	CTIB	IBCN	SPDB	EBBC
	2020	ICBC	ABCI	BCHO	CCBH	BCOH	IBCN	CTIB	SPDB	CMSB	EBBC
	2021	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	CTIB	SPDB	CMSB	EBBC
	2022	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC

参数	1	2	3	4	5	6	7	8	9	10
基准情形	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CMSB	EBBC	CTIB
$μ = 0.00001 x = 0.001$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.01$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.0001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC

参数	1	2	3	4	5	6	7	8	9	10
基准情形	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CMSB	EBBC	CTIB
$μ = 0.00001 x = 0.001$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.01$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.1$	ICBC	CCBH	ABCI	BCHO	BCOH	IBCN	SPDB	CTIB	EBBC	CMSB
$μ = 0.0001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	EBBC	CTIB	CMSB
$μ = 0.001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	CTIB	SPDB	EBBC	CMSB

参数	1	2	3	4	5	6	7	8	9	10
基准情形	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CMSB	EBBC	CTIB
$μ = 0.00001 x = 0.001$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.01$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	SPDB	CTIB	CMSB	EBBC
$μ = 0.00001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	CMHO	SPDB	CTIB	CMSB
$μ = 0.0001 x = 0.1$	ICBC	ABCI	CCBH	BCHO	BCOH	IBCN	CMHO	SPDB	CTIB	CMSB
$μ = 0.001 x = 0.1$	ICBC	ABCI	BCHO	CCBH	BCOH	CTIB	CMHO	SPDB	EBBC	HXBJ

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序号	事件	事件发生时间	冲击渠道
1	A股熔断，人民币贬值	2016.01.04-2016.01.08	渠道2
2	英国脱欧公投	2016.06.23	渠道1
3	美国总统大选	2016.11.09	渠道3
4	意大利修宪公投	2016.12.04	渠道1
5	英国启动脱欧	2017.03.29	渠道1
6	美国就乌克兰和克里米亚问题对俄施压	2018.01.26	渠道1
7	英国正式脱欧	2020.01.31	渠道1
8	美国总统大选	2020.11.03	渠道3
9	日本干预汇市	2022.09-2022.10	渠道1
10	中美贸易摩擦	2018.03.22—	渠道3
10-1	美国重提加征关税，中美贸易摩擦正式开始	2018.07.06
10-2	美国再加征关税，中国同加征关税	2018.08.23
10-3	美国再加征关税并将华为公司列入黑名单	2019.05.15
10-4	美国再宣布加征关税	2019.08.15
11	新冠肺炎疫情	2019.12-2022.12.14	渠道3
11-1	首次出现不明肺炎病例引发关注	2019.12.29	渠道2
11-2	美国疫情感染最高峰	2022.01.08	渠道3
12	美联储6次调息	2022.03.17-2022.12.15	渠道3
12-1	美联储利率决议，上调25个基点	2022.03.17
12-2	美联储利率决议，上调50个基点	2022.05.05
12-3	美联储利率决议，上调75个基点	2022.06.16
12-4	美联储利率决议，上调75个基点	2022.07.28
12-5	美联储利率决议，上调75个基点	2022.09.22
12-6	美联储利率决议，上调75个基点	2022.12.15
13	俄乌冲突	2022.02—	渠道3

冲击渠道	冲击强度	加大监管力度的外汇做市商	下调监管水平的外汇做市商
渠道1	/	CTIB	CMSB、EBBC
渠道2	弱冲击	CTIB	CMSB、EBBC
渠道2	强冲击	CTIB	CMSB、SPDB
渠道3	弱冲击	CTIB	CMSB、EBBC
渠道3	强冲击	CTIB、HXBJ、CMHO	CMSB、SPDB、IBCN

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Research on Systemic Risk Measurement of China’ s Foreign Exchange Market Based on Knowledge Graph Network Characteristics

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事件		1	2	3	4	5	6	7	8	9	10
1	冲击前冲击后	NJCB	DESZ	NBCB	CMHO	EBBC	CCBH	HXBJ	BCOH	ICBC	CMSB
1	冲击前冲击后	DESZ	NJCB	EBBC	NBCB	CCBH	HXBJ	CMSB	CMHO	BCOH	ICBC
2	冲击前冲击后	NJCB	BCOH	CMHO	NBCB	CCBH	CMSB	ICBC	ABCI	HXBJ	CTIB
2	冲击前冲击后	NJCB	NBCB	CMHO	EBBC	BCOH	HXBJ	DESZ	CCBH	IBCN	CTIB
3	冲击前冲击后	NJCB	DESZ	NBCB	CMHO	BCOH	CMSB	CCBH	ICBC	EBBC	CTIB
3	冲击前冲击后	NBCB	NJCB	EBBC	DESZ	HXBJ	CMHO	CCBH	CTIB	CMSB	ICBC
4	冲击前	NJCB	NBCB	CMHO	BCOH	DESZ	CCBH	HXBJ	EBBC	CMSB	ICBC
4	冲击后	NJCB	NBCB	DESZ	EBBC	HXBJ	CMHO	CCBH	BCOH	CMSB	ICBC
5	冲击前	DESZ	NJCB	CMHO	NBCB	EBBC	BKSH	BCOH	HXBJ	CCBH	ICBC
5	冲击后	DESZ	NJCB	EBBC	CMSB	NBCB	CMHO	BKSH	BCOH	CCBH	HXBJ
6	冲击前	NJCB	BKSH	NBCB	CCBH	BCOH	EBBC	HXBJ	ICBC	CMSB	DESZ
6	冲击后	DESZ	NJCB	BKSH	NBCB	CCBH	EBBC	CMHO	ICBC	HXBJ	BCOH
7	冲击前	NJCB	BKSH	BCOH	QDRC	IBCN	CMSB	HXBJ	ABCI	EBBC	BCHO
7	冲击后	NJCB	BKSH	BCOH	NBCB	CMHO	EBBC	HXBJ	CMSB	ICBC	IBCN
8	冲击前	DESZ	CMHO	NJCB	NBCB	CCBH	BKSH	EBBC	HXBJ	BCOH	QDRC
8	冲击后	NJCB	BKSH	NBCB	CMHO	EBBC	DESZ	CCBH	HXBJ	BCOH	CMSB
9	冲击前冲击后	NBCB	DESZ	NJCB	CMHO	BKSH	BCOH	EBBC	HXBJ	CMSB	CCBH
9	冲击前冲击后	NBCB	DESZ	CMHO	EBBC	HXBJ	BKSH	NJCB	CZSB	CCBH	CTIB