Network analysis of risk in shareholdings by life insurers
Masayasu Kannoa,∗aCollege of Commerce, Nihon University, Tokyo 157-8570, Japan

Abstract
This study assesses the interconnectedness of credit risk exposures in a tripartite network of cross-shareholdings among insurers, banks, and firms inJapan’s stock market during the fiscal years 2008–2015. We use consistentmeasures: credit risk exposure by PD (probability of default)/LGD (lossgiven default) approach in Basel III and RORA (return on risk-weighted
assets). We conduct a credit risk analysis of the risk exposures in the crossshareholdings. The result shows that by following the PD/LGD approach,the credit risk weights become approximately 1.5 to 5 times as large as by
the transitional risk weight method. The mean exposure-weighted risk forthe firm’s shareholdings is 1.67 times as large as the bank’s and the insurer’s.
We analyze the network structure of the cross-shareholdings using network
centrality measures. Our analysis can provide each entity with important
implications on credit risk management in their cross-shareholdings.
Keywords: credit risk; cross-shareholding; interconnectedness; return on
risk-weighted assets (RORA); centrality measure
JEL classification: G32; G10; D85; L14∗

Corresponding author. E-mail address: kanno.masayasu@nihon-u.ac.jp

1. Introduction
In past decades, insurers, banks, and firms in Japan have traditionallyadopted a cross-shareholding corporate governance structure. In terms ofbusiness relations between banks and firms, shareholdings enable banks andfirms to maintain good relationships with counterparties. By contrast, fromthe perspective of insurers, especially life insurers that are mostly mutual
insurance firms, their shareholdings are in portfolio investments rather thancross-shareholdings. However, as insurers hold large number of shares aspolicy owners, they virtually conduct both cross-holdings and portfolio investment. Hence, cross-shareholdings in this study include not only mutualshareholdings but also unilateral ones.
Currently, banks are regulated based on the Basel III framework (BCBS,
2005). The ‘transitional measure’ of equity exposures of banks adopting
an internal ratings-based approach (i.e., IRB approach) ended in June 2014.
This triggered a large increase of risk weights in the assets of Japanese banks.
Hence, the dissolution of shareholding stocks by major banks adopting the
IRB approach is of concern. As alternatives for measuring risk weights onequities under Basel III, banks receive the ‘Market-based approach’ and‘PD/LGD approach’1 . Both approaches largely increase the risk weights
approximately 1.5 to 4 times (i.e., 150–400%) compared to 100% in the transitional measure.
By contrast, insurance regulation in Japan depends on the Japan local
supervisory framework based on the ‘solvency margin ratio.’ This framework
is simple and a so-called first-generation solvency regulation,2 , which is similar to the Basel I in international banking regulations. ‘Solvency margin
standard’ was introduced for both life and general insurance firms in fiscal
year3 1996. The solvency margin is calculated as the ratio of solvency margin divided by the half of risk amount. In terms of equity exposure, the risk
amount is calculated on the risk of occurrence of loss caused by excessivechanges in market value in market risk management.

1This approach calculates risk-weighted lending assets using the probability of default(PD) and loss given default (LGD).
2
An example of second-generation solvency regulation, the Swiss Solvency Test is a risk
based capital standard for insurance firms in Switzerland, in use since 2006. In addition,
the EU’s Solvency II regulation came into force on January 1, 2016.
3
Japan’s fiscal year runs from April 1 to March 31.

2

Triggered by the bankruptcy of Lehman Brothers, many insurers, banks,
and firms with cross-holding stocks suﬀered significant capital losses, withsome booking impairment losses. In addition, in response to recent requests
of investors for highly transparent management, in June 2015, Japanese firms
adopted ‘Japan’s Corporate Governance Code (JCGC)’ (Council of Experts
Concerning the Corporate Governance Code, 2015), which requires that management explain the purposes and reasons for holding shares of listed stocks.
Principle 1.4 of the JCGC mentions cross-shareholdings as follows: whenfirms hold shares of other listed firms as cross-shareholdings, they should
disclose their policy with respect to their holdings. In addition, the board
should examine the mid- to long-term economic rationale and outlook ofmajor cross-shareholdings annually, considering both associated risks andreturns. The board should present detailed explanations of the objective
and rationale behind cross-shareholdings after this annual examination.
Inspired by the JCGC, three mega-bank groups (i.e., Mitsubishi UFJ
Financial Group, Sumitomo Mitsui Financial Group, and Mizuho Financial
Group) adopted return on risk-weighted assets (RORA). Firms generally
adopt return on assets (ROA) as a profitable financial ratio. However, thedenominator of the ROA, the total asset amount, is less sensitive to risk. The
RORA is a risk-adjusted profit indicator, and hence, much more preferable
than the ROA or the return on equity capital (ROE).
We contribute to the literature by providing quantitative insight intocross-shareholdings considering credit risk management. Japan’s stock market is a good example of cross-shareholding practices in the world.
Our study assesses the interconnectedness in the tripartite network of
insurers, banks, and firms by using consistent measures common to all three
entities: credit risk exposure (i.e., credit risk-weighted asset) measured by
the PD/LGD approach and RORA during the period (FY 2008–2015) fromthe global financial crisis to the aftermath of JCGC implementation.
First, we conduct a credit risk analysis using the PD/LGD approach inBasel III. We consider an equity exposure same as a lending exposure. In
addition, by adopting the RORA, insurers, banks, and firms can assess their
exposures using one consistent risk-sensitive measure.
Second, we analyze interconnectedness in the cross-shareholding network
using various network centrality measures (Jackson, 2010; Kanno, 2015a;
Kanno, 2016). We base the network analysis on data of bilateral shares of alllisted Japanese entities in the Nikkei NEEDS Financial QUEST database.
Hence, our datasets cover almost all bilateral shareholding contracts among3

insurers, banks, and listed firms with at least one contract with a bank and/or
an insurer.
Section 2 reviews prior literature on cross-shareholding and interconnectedness in various financial networks. Section 3 contains the credit risk analysis using some measures. Section 4 presents the network structure analysis
of cross-shareholding. Section 5 conducts stress test and Section 6 concludes
the study.
2. Literature review
The finance literature on cross-shareholding contains studies over the pastfew decades from several countries, including Yonezawa and Miyake (1998)(Japan), Suetorsak (2007) (Japan), Almeida et al. (2011) (Korea), Choi etal. (2014) (Japan), and Kanno (2016). However, none of these studies except
Kanno (2016) examines cross-shareholding from the network perspective.
By contrast, most network analyses of cross-shareholding are from theperspective of econophysics. Ma et al. (2011) analyze the relationships
among domestic mutual investments in China based on the cross-shareholding
networks of listed firms. Li et al. (2014) analyze the topological properties
and the evolution of cross-shareholding networks of listed firms in China.
Our study contributes to cross-shareholding literature by analyzing the
network structure of cross-shareholding by combining credit risk analysis with
network analysis. Network analysis is a highly eﬀective approach in examining the interconnectedness of firm relationships in stock markets, which represent complex contract networks using sets of ‘nodes’ connected by ‘edges.’
In a cross-shareholding stock network, a node represents a shareholder oran issuer and an edge represents the cross-shareholding relationship between
two entities.
De Masi and Gallegati (2012) and Lux (2014) are extant studies on bankfirm bipartite credit networks. Lux (2014) studies a stochastic model of
network for credit linkages between banks and non-financial firms based onwell-known and plausible stylized facts confirmed by recent studies using
comprehensive data sets for several industrial countries.
An analytically tractable example of financial networks is the interbank
network characterized by bilateral exposure in the interbank market. Prior
studies of financial networks adopt two approaches. The first approach assesses the strength of the contagion channels and network resilience by observing the responses of the financial network structures to shocks. Intro4

ducing a shock assumes a specific transmission mechanism, such as defaults
by counterparties. Alves et al. (2013) refer to this approach as ‘dynamic
network analysis.’ Elsinger et al. (2006), Cocco et al. (2009), and Haldane
and May (2011) analyze contagion eﬀects in their network analyses.
The second approach describes network structures using topological indicators, often relating these indicators to model graphs based on network
theory. This approach does not assume a mechanism by which shocks propagate within the network; therefore, it is referred to as ‘static network analysis’ (Alves et al., 2013). Eisenberg and Noe (2001), Boss et al. (2004), andKanno (2015a, 2016) are examples of studies based on this approach. Our
study adopts the static network analysis.
3. Credit risk analysis
We analyze the credit risk based on the RORA by calculating risk-weighted
assets based on the Basel III – PD/LGD approach, using a large-scale database.
3.1. Methodology
3.1.1. Risk-weighted assets for equity exposures
First, we calculate risk-weighted assets for equity exposures. In Basel III,the methodology and minimum requirements for the PD/LGD approach forequity exposures are the same for the IRB foundation approach for corporate
exposures, subject to the following specifications. The bank’s estimate of the
PD of a firm that holds an equity position must satisfy the same requirements
as the bank’s estimate of the PD for its lending exposures. There is no IRB
advanced approach for equity exposures, given the 90% LGD assumption.
LGD would be assumed as 90% in deriving the risk weight for equity
exposures. Minimum risk weights are set under the PD/LGD approach.
When the sum of UL and EL associated with the equity exposure results inless capital than would be required from applying one of the minimum risk
weights, the minimum risk weights must be used. The minimum risk weightof 100% applies to policy ownership securities.
For all other equity positions, including net short positions, capital charges
calculated under the PD/LGD approach may be no less than the capitalcharges calculated under a simple risk weight method using a 200% risk
weight for publicly traded equity holdings and a 300% risk weight for all5other equity holdings. The maximum risk weight of the PD/LGD approachfor equity exposures is 1250%.4
For equity exposures not in default, the formula for calculating riskweighted assets (RWA) is as follows:
RWA = K × 12.5 × EAD,

(1)

where EAD is the exposure at default and capital requirement (K) is as
[ (√
]
√
)
1
R
K = LGD Φ
G(PD) +
G(0.999) − PD × f (M, PD),
1−R
1−R
where G is the inverse of standard normal cumulative distribution, M is theeﬀective maturity, and LGD is 90%. The adjustment term (f ), maturity
adjustment (b), and correlation (R) are as follows:
1 + (M − 2.5)b(PD)1 − 1.5b(PD)b(PD) = (0.11852 − 0.05478 × ln(PD))2()
1 − e−50PD
1 − e−50PD+ 0.24 · 1 −R(PD) = 0.12 ·,1 − e−50
1 − e−50

f (M, PD) =where, for exposures to small- and medium-sized entities (SME) borrowers,
defined as firm exposures in which the reported sales of the consolidated
group is less than 500 million yen, a firm-size adjustment (i.e., 0.04 × (1 −(S − 5)/45)) is made to the corporate risk weight formula for exposures to
SME borrowers. S is expressed as total annual sales in billions of yen withvalues of S falling in the range of equal to or less than 5 billion yen, or greaterthan or equal to 500 million yen. Reported sales of less than 500 million yen
will be treated as equivalent to 500 million yen for the firm-size adjustmentof SME borrowers. Thus, the correlation for exposures to SME borrowers is

4

In practice, if there is both equity and IRB credit exposure to the same counterparty,
a default on the credit exposure would trigger a simultaneous default, for regulatory purposes, on the equity exposure.

6

as follows:()()
1 − e−50PD
1 − e−50PD
S−5
R(PD) = 0.12 ·+ 0.24 · 1 −− 0.04 · 1 −.
1 − e−50
1 − e−50
45
3.1.2. RORA
Next, we calculate the RORA for the period FY 2008–2015. Essentially,
the numerator return should include capital gain (loss) and income gain
(loss). However, because we cannot obtain information of the trade dates ofeach firm and the outstanding at the dates for capital gain (loss), we utilize
only the dividend data for income gain (loss).
3.2. Data for credit risk analysis
We use firm-level financial data and probability of default (PD) data for
FY 2008–2015 for credit risk analysis.
The analysis requires bilateral outstanding data for equity risk exposures,
which we obtain from the Nikkei NEEDS Financial QUEST database provided by Nikkei Inc., a Japanese newspaper firm. Hence, we calculate themarket value (i.e., EAD in equation (1)) of a holding stock, multiplying theshare by the market price at each evaluation time point. The Nikkei NEEDS
Financial QUEST database comprises of some sub-databases. Two subdatabases are firms’ shareholding database and the blockholding database.
The former does not include shareholding data of insurers and banks; however, the latter includes the shareholdings of insurers and banks as blockholders. Hence, we extract two datasets from these sub-databases for ourstudy. As shown in Figure 1, one dataset is the firms’ shareholding dataset(‘FH’) and the other is the blockholding dataset (‘BH’).
The firms’ shareholding dataset contains information on listed firms thatown shares of listed entities such as insurers and banks. However, as shareholdings between firms are outside the scope of our study, we exclude themfrom the dataset. The dataset contains the shares of 2,936 Japanese listed
firms, as of June 2016, covering fiscal years (FY) 2008–2015 (i.e., from thestart of April 2008 to the end of March 2016). The dataset contains information on stock investments, the stocks of aﬃliates, and cross-holding stocks
as a sub-database, and firms’ shareholding database. The label ‘policy ownership securities’ in this sub-database identifies the cross-holding stocks heldby a firm.
By contrast, the blockholding dataset contains information on insurers
7Figure 1: Two shareholdings in the dataset

and banks as blockholders ranked in the top thirty by entity. The banks
include city banks, regional banks, second-tier regional banks, and shinkinbanks.5 The insurers include both life insurers and non-life insurers. However, because shareholdings between firms are beyond the scope of our study,
we exclude firm blockholders from the dataset.
We also obtain dividend data from the Nikkei NEEDS Financial QUEST
database. In addition, we obtain data by entity for the probability of default(PD) from the Credit Research Initiative (CRI) database published free ofcharge by the National University of Singapore (NUS)6 .

5As trust funds of foreign banks do not have any identification codes in the database,we exclude them from the dataset.
6
The initiative is a non-profit undertaking by the Risk Management Institute at NUS,
and seeks to promote research and development in the field of credit risk. The CRI
promotes the PD model (NUS, 2013) as its foundation, which was developed using a
database of over 60,000 listed firms in Asia Pacific, North America, Europe, Latin America,
the Middle East, and Africa.

83.3. Analysis results
We present estimation results of risk weight, risk-weighted assets for equity exposures, and RORA.
3.3.1. Risk weight
Figure 2 and Table 1 denote the scatter plot for risk weight and mean for
exposure-weighted risk weight by fiscal year for the period FY 2008–2015.
The ‘PD/LGD approach’ largely increases the risk weights approximately
1.5 to 5 times (i.e., 150–500%) as large as (i.e., 100%) in the transitionalrisk weight method, which utilizes the standardized approach of credit risk.
Especially, the maximum risk-weight reached over 500% for the result in FY
2008 corresponding to the global financial crisis. As Basel II applied thetransitional risk weight method during this time, equity exposures did not
substantially aﬀect Japanese banks.
In addition, the mean exposure-weighted risk weight for the firm’s shareholdings is 1.67 times as large as that of the blockholdings. For crossshareholdings, it is evident that firms hold stocks with larger risk weights thaninsurers and banks. Therefore, insurers and banks use cross-shareholdings for
credit risk management, whereas firms have strong ties with their corporategroup or keiretsu and use cross-holdings as anti-takeover measures.
Table 1: Mean exposure-weighted risk weight expressed as percentage by
fiscal year

FH
BH

2008 2009 2010 2011 2012 2013
300 252 225 232 170 210
240 116 106 108 105 137

2014 2015
137 195
107 113

Mean
215
129

Note: Abbreviations: FH: firm’s shareholding dataset; BH: blockholding
dataset. ‘Mean’ indicates the overall mean for the period FY 2008–2015.

3.3.2. Risk-weighted assets for equity exposures
Table 2 reports the quartiles and mean & standard deviation in the upper
tier and the sums of original exposures by shareholding purposes in the lower
tier, related to credit risk-weighted assets (i.e., credit risk exposures) at theend of fiscal years 2008–2015. Blockholdings correspond to shareholdings
9

2008

2009

6

2010

4

3.5

3

3

2.5

2.5

2

2

1.5

1.5

5
3

4
3

2

2
1

0

0.1

0.2

1

2012
3.5

0

0.05

3

1

0.1

2013
3

2011

3.5

0

0.05

1

0

2014
3.5

0.05
2015

4

3

2.5

2.5

3

2.5
2

2

2
1.5

1.5
1

0 0.01 0.02

1

2

1.5
1

0 0.01 0.02

0 0.01 0.02

1

0

0.05

Figure 2: Risk weight scatter plot by fiscal year
Note: Risk weight is calculated based on the risk weight function in the IRB
approach to capital requirements for credit risk in Basel III for FY 2008–2015.

10

designated ‘BH’ in Figure 1. By contrast, temporary investments, policy
ownership securities, portfolio investments, and deemed securities correspondto shareholdings designated ‘FH’ in Figure 1. In addition, Figure 3 illustrates
the percentile distribution of bilateral credit risk-weighted assets by year.
From the upper tier of Table 2 and Figure 3, all of the exposure sizes arenear zero at the 75th percentile; however, the sizes increase sharply from the
99.5th percentile to the maximum, and range from 509 billion in FY2009 toa maximum of 53,225 billion Japanese yen (JPY) in FY2008. This means
that, for the purpose of reducing credit risk exposures, blockholdings decreased sharply just after the Lehman Brothers bankruptcy. By contrast,
since FY2010, the first motivation of shareholdings by firms has been policy
ownership.
Furthermore, from the lower tier of Table 2, the credit risk exposure sizein the entire network decreased sharply to about one-tenth from FY2008 to
FY2009 owing to the global financial crisis, and remains unchanged. After FY2012, it decreased slightly because the outstanding investment funds
decreased (Flow of Funds Accounts Statistics issued by the Bank of Japan)
and Japan’s Corporate Governance Code went into eﬀect (Council of Experts
Concerning the Corporate Governance Code, 2015).
3.3.3. RORA
The upper tier of Table 3 reports the descriptive statistics of the RORA.
In addition, Figure 4 shows the RORA histogram by fiscal year. The RORA
almost remains in the 0–5% range; nevertheless, the mean gradually increases
for the period FY2008–2015. This means growth in credit risk-adjusted return for shareholdings as a whole, among insurers, banks, and firms.
In addition, we carefully examine features of the RORA in the lower tierof Table 3. The ‘BH’ part in the lower tier of Table 3 denotes the RORA of
the credit risk exposure of a firm by a mega bank group/major life insurerin each upper row and the credit risk-weighted asset amount in billions of
yen of the bank/insurer in each lower row. The ‘PI’ part in the lower tier ofTable 3 denotes the RORA of the credit risk exposure of investment securities
of a mega bank group/major life insurer by a firm in each upper row andthe credit risk-weighted asset amount in billions of yen of the bank/insurerin each lower row. The ‘CH’ part in the lower tier of Table 3 denotes the
RORA relating to policy ownership securities of a mega bank group/major
life insurer by a firm in each upper row and the credit risk-weighted asset
amount in billions of yen of the bank/insurer in each lower row.
11

12

2008
0.1
0.3
1.3
53,224.9
27.3
755.0
417,452.1
0.4—
7,841.5
0.0425,294.0

2009
0.1
0.3
1.1
508.8
2.1
9.6
28,012.6
0.3
5,623.2
6,738.8
0.040,374.9

2010
0.1
0.2
0.7
7,702.3
2.2
62.3
33,160.8
0.3
5,337.6
4,946.6
810.3
44,255.6

2011
0.1
0.2
0.7
2,486.3
2.0
31.8
28,151.1
1.2
5,513.8
5,502.0
1,153.040,321.2

2012
0.1
0.2
0.8
18,759.0
4.1
159.6
80,262.3
1.0
5,519.3
5,394.6
1,150.8
92,327.9

2013
0.1
0.3
1.0
12,682.4
3.8
114.1
60,172.9
0.0
7,007.6
2,126.3
1,497.5
70,804.3

2014
0.1
0.3
1.0
3,352.8
3.0
37.5
42,906.7
0.0
5,648.1
1,134.1
1,109.5
50,798.4

2015
0.1
0.2
1.0
1,474.8
2.5
18.9
29,685.9
0.0
5,592.1
1,102.6
1,169.1
37,549.7

Note: Abbreviations: S.D.: standard deviation; FH: firm’s shareholding dataset; BH: blockholding dataset. Blockholdings
by foreign investment funds are excluded. There is no classification of ‘policy ownership securities’ in FY2008 on the
database.

25%-tile
Median75%-tile
Maximum
Mean
S.D.
Blockholdings (BH)
Temporary Investments (FH)
Policy ownership securities (FH)
Portfolio investments (FH)
Deemed securities (FH)
Total amount

Table 2: Descriptive statistics of bilateral credit risk-weighted assets (in billions of JPY)

x 10

4

6

amount

4

2

0
2008

100
2010

99.8

2012
99.6

2014

percent

fiscal year

Figure 3: Bilateral credit risk-weighted assets distribution
Note: Exposure amounts are expressed in billions of JPY. The distribution shows
the range from the 99.5th percentile to the 100th percentile.

Consequently, the average RORA for blockholdings (BH) of the megabank groups (MUFJ FG, SMFG, and Mizuho FG) is 0.93 to 1.45 times as highas the average RORA for cross-shareholdings (CH) of bank or insurer stocks
by firms. This means that cross-shareholdings are against firms’ interests interms of a risk-adjusted return. By contrast, the RORA of blockholdings byfirms is higher than the RORA of portfolio investment and cross-shareholdingof bank or insurer stocks by firms for FY2008–2013. Hence, firms obtain highprofits if they hold shares as blockholders. However, they suﬀer from the low
profits from cross-shareholdings.
Furthermore, the outstanding credit risk-weighted asset amounts for megabank groups and major insurers substantially reduce to less than one-tenthjust after the global financial crisis and slightly decrease after the implementation of JCGC. By contrast, the outstanding cross-holdings of insurers’
and banks’ stocks by firms were almost unchanged. Considering their business relations with insurers or banks, firms cannot easily reduce outstanding
cross-holdings, even after JCGC.
In addition, the figure in each upper row in the rightmost column denotes
the correlation between the RORA(t − 1) and the ARRA(t) at t = the end
13

of March 2010 to March 2016. The figure in each lower row in the rightmost
column denotes the correlation between the RORA(t − 1) and the ARRA(t)at t = the end of March 2016. We define the correlation as follows:
Corr. = correlation(RORA(t − 1), ARRA(t)),(2)

where ARRA(t) denotes the annualized return of risk-weighted assets calculated in equation (1) at each end time t for the period FY2010–2015. As
insurers or banks validate the investment performance of their equity portfolios in the previous year and decide its holding policy the following year,
we can reasonably assume that ARRA lags one year behind RORA.
All values in the Corr. part of Table 3 indicate positive correlations exceptof the cross-shareholding and the portfolio investment relating to Dai-ichi
Life. The result demonstrates that both insurers and banks initiate risksensitive cross-shareholdings. Especially, they dissolve cross-shareholdings
after JCGC implementation in June 2015, as Corr. at t = the end of FY2016
are 100% across all insurers and banks except of the cross-shareholding andthe portfolio investment relating to Dai-ichi Life.

frequency

6000
4000
2000
0
0

2008

0.05

2010
2012

0.1
2014

0.15

fiscal year

RORA

Figure 4: RORA histogram by fiscal year

14

Table 3: Descriptive statistics of RORA, credit risk-weighted asset amounts(RWA), and correlation between RORA and annual return of RWA
25%-tile
Median75%-tile
Maximum
Mean
S.D.
MUFJ
BH
FG
PI
CH
SMFG

BH
PI
CH

Mizuho
FG

BH
PI
CH

NipponLife
Dai-ichi
Life

BH
BH
PI
CH

Sumitomo BH
Life
All firms BH
PI
CH

2008 2009 2010 2011 2012 2013 2014 2015
Corr.
0.58% 0.73% 1.00% 1.04% 1.00% 1.06% 1.02% 1.14%
1.06% 1.08% 1.39% 1.40% 1.53% 1.23% 1.59% 1.65%
1.90% 1.70% 1.74% 1.89% 1.85% 1.74% 1.93% 2.15%
11.33% 9.82% 7.80% 14.63% 10.37% 6.50% 8.58% 9.94%
1.36% 1.31% 1.46% 1.52% 1.48% 1.39% 1.54% 1.68%
1.18% 1.03% 0.97% 1.01% 0.92% 0.84% 0.85% 0.94%
1.83% 1.68% 1.89% 1.99% 1.88% 1.81% 1.63% 1.84%
57.2%
95,609 3,907 4,670 3,705 10,390 11,741 4,751 3,714 100.0%
0.81% 0.87% 1.39% 1.04% 1.58% 1.06% 1.87% 1.46%
57.9%
2,256 2,152 1,233 1,723 1,190 646
230
284
100.0%
0.81% 0.87% 1.39% 1.04% 1.58% 1.06% 1.87% 1.46%
57.9%
2,256 2,152 1,233 1,723 1,190 646
230
284
100.0%
1.74% 1.68% 1.92% 1.99% 1.88% 1.82% 1.64% 1.79%
39.4%
30,854 1,964 1,226 4,211 2,656 5,885 2,899 2,596 100.0%
0.58% 1.08% 1.39% 1.18% 1.07% 1.30% 2.83% 2.46%
43.5%
1,112 850
619
747
1,017 205
83
101
100.0%
0.58% 1.08% 1.39% 1.18% 1.07% 1.30% 2.83% 2.46%
43.5%
1,112 850
619
747
1,017 205
83
101
100.0%
1.57% 1.54% 1.83% 1.94% 1.76% 1.69% 1.55% 1.67%
68.4%
523
343
308
299
19,192 1,024 2,461 2,373 100.0%
1.76% 1.28% 1.27% 1.44% 1.03% 1.14% 1.70% 2.15%
52.1%
1,136 1,061 705
811
1,104 339
204
162
100.0%
1.76% 1.86% 2.16% 2.29% 1.58% 1.14% 1.70% 2.15%
79.0%
1,136 1,061 705
811
1,104 339
204
162
100.0%
1.76% 1.67% 1.88% 2.00% 1.87% 1.78% 1.60% 1.69%
72.9%
116,4186,674 4,526 5,233 10,955 6,920 7,485 5,574 100.0%
1.74% 1.65% 1.87% 2.00% 1.88% 1.81% 1.63% 1.77%
22.2%
28,203 2,675 1,574 3,790 2,484 2,121 2,378 1,937 100.0%
0.01% 0.01% 0.01% 0.77% 0.76% 1.07% -32.6%
102
99
99
77
64
57
-100.0%0.01% 0.01% 0.01% 0.77% 0.76% 1.07% -21.6%
102
213
106
77
64
57
-100.0%1.58% 1.53% 1.73% 1.81% 1.65% 1.62% 1.39% 1.50%
47.8%
20,585 808
548
506
621
738
1,062 929
100.0%
1.67% 1.63% 1.83% 1.93% 1.79% 1.73% 1.57% 1.70%
76.5%
417,45228,013 33,161 28,151 80,262 60,173 42,907 29,686 100.0%
0.84% 0.93% 1.16% 1.22% 1.22% 1.11% 1.50% 1.65%
28.6%
7,841 6,739 4,947 5,502 5,395 2,126 1,134 1,103 100.0%
0.84% 1.06% 1.37% 1.42% 1.38% 1.14% 1.52% 1.65%
44.1%
7,841 6,739 4,947 5,502 5,395 2,126 1,134 1,103 100.0%

Note 1: Abbreviations: S.D.: standard deviation; Corr.: correlation. BH means the shareholdings by blockholders,such as mega-bank groups or major life insurers. PI denotes the shareholdings of investment securities issued
by three mega-bank groups or Dai-ichi Life. CH means the cross-holdings of policy ownership securities issued
by three mega-bank groups or Dai-ichi Life. Because Nippon Life and Sumitomo Life are not joint-stockcompanies but mutual insurance firms, they issue no policy ownership security.
Note 2: The figure in each upper row of the lower tier of this table denotes the RORA of a bank/insurer, whereas thefigure in each lower row denotes the credit risk-weighted asset amount of the bank/insurer in billions of yen.
The figure in each upper row in the rightmost column denotes the correlation between the RORA(t − 1) and the
ARRA(t) at t = the end of March 2010 to March 2016. The figure in each lower row in the rightmost column
denotes the correlation between the RORA(t − 1) and the ARRA(t) at t = the end of March 2016.

15

4. Network analysis
This section describes the analysis of the cross-shareholding structures(including unilateral shareholdings) of the bank-insurer-firm tripartite network in Japan’s stock market. This analysis is based on credit risk exposures(i.e., credit risk-weighted assets), diﬀerent from nominal exposures examinedin most extant literature on credit risk management.
4.1. Data for network analysis
The following (N × N ) matrix X represents the cross-shareholding relationships in Japan’s stock market:
x11 · · · x1j · · · x1N
.. 
..
..
.
 ..
.
..
. 
.
 .


X =  xi1 · · · xij · · · xiN  ,
(3)
 .

.
.
.
.
.
.
.
.
.
 .
.
.
. 
.
x N 1 · · · xN j · · · xN N
where xij denotes the outstanding shares of firm i in the shareholding offirm j. Summation across row i gives firm i’s total outstanding shares of its
counterparties, and summing down column j gives firm j’s total outstanding
shares held by counterparties. Hence, matrix X is asymmetric.
As the analysis requires bilateral outstanding data for the credit risk
exposure matrix X, we utilize the details of outstanding data by shareholding
purposes shown in Table 2.
Relation between keiretsu and data. Japan has various types of firm groups.
A keiretsu is a group of firms with interlocking business relationships andshareholdings. The keiretsu dominated the Japanese economy in the second
half of the 20th century.
Two types of keiretsu, horizontal and vertical, can be further categorized into Kigyo shudan (‘horizontally diversified business groups’), Seisan
keiretsu (‘vertical manufacturing networks’), and Ryutsu keiretsu (‘vertical
distribution networks’). As our study focuses on the bank-insurer-listed firm
tripartite network as shown in Figure 1, we do not treat vertical networks.
The primary characteristic of a horizontal keiretsu, also known as a financial keiretsu, is to maintain business relationships with a bank by crossshareholdings with other firms.
16

The Japanese recession in the 1990s had a profound eﬀect on the financial
keiretsu. Many of the largest banks were hard-hit by non-performing loans
and forced to merge or go out of business. This blurred the lines between theindividual keiretsu: three mega-bank groups (i.e., Mitsubishi UFJ Financial
Group, Sumitomo Mitsui Financial Group, and Mizuho Financial Group)
descended from the big six keiretsu: Mitsui, Mitsubishi, Sumitomo, Fuyo,
Sanwa, and Dai-Ichi Kangyo banks.
4.2. Methodology and analysis results
We calculate the network statistics and centrality measures for FY2008–2015 (Table 4). Network size indicates the total number of links in the crossshareholding network. Table 4 indicates that after FY2008, the network size
remained unchanged as a whole. In addition, we calculate five centrality
measures: degree centrality, eccentricity, closeness centrality, betweenness
centrality, and eigenvector centrality. Table 4 reports the averages for eachtype of centrality.
‘Direct’ centrality measures capture the level of interconnectedness ina local region based on adjacent connections, and are proxies for crossshareholding influence. These measures are degree and eigenvector centrality.
By contrast, ‘indirect’ centrality measures enable the analysis of a counterparty’s exposure in the entire network by its distance to all other firms. We
use these measures to evaluate information value-oriented networks. Eccentricity, closeness, and betweenness are examples of indirect centrality measures, and show how close a firm node is to other nodes in the entire network
to reflect the importance of one firm in the network (Renneboog and Zhao,2014).
It is important to understand that managerial influence and information,such as firm importance, are two aspects of the same network. The two measures are not exclusive; the direct measures that express a firm’s managerial
influence on its counterparties also have the ability to capture information,
which could benefit the firm. Nonetheless, the correlation between direct
and indirect centrality measures is generally low (Kanno, 2016b), suggestingthat direct and indirect measures indeed capture diﬀerent properties of the
network.
In terms of degree centrality, the ‘degree’ of a firm is the number of shareholding links connected to the firm. The ‘out-degree’ is a firm’s total numberof shareholdings and the ‘in-degree’ is a firm’s total number of shareholdings
held by other firms. In a directed graph, which depicts a set of firms in
17

Table 4: Cross-shareholding network structure based on credit risk exposuresamong Japanese insurers-banks-listed firms.
FY Network size
2008
14,925
2009
14,581
2010
15,052
2011
15,378
2012
16,917
2013
16,3472014
15,6062015
13,892Degree
9.88
9.65
9.96
10.18
11.20
10.82
10.33
9.20

Ecc.
3.467
3.423
3.098
3.084
3.470
3.525
3.798
2.808

Clo.
1.909
1.877
1.563
1.536
1.778
1.809
1.743
1.452

Betw. Eigenv.
0.00128 0.0215
0.00126 0.0221
0.00136 0.0278
0.00137 0.0296
0.00137 0.0276
0.00138 0.0266
0.00138 0.0274
0.00144 0.0291

Note: Abbreviations: Ecc.: Eccentricity; Clo.: Closeness; Betw.: Betweenness; Eigenv.:
Eigenvector. Network size is the total number of shareholding relationships in the
network.

which all shareholdings are directed from one firm to another, each firm has
a maximum of two degrees for each shareholding. The total degree of a firm
is the sum of its in- and out-degrees. The degree of a firm is a proxy variable
for its interconnectedness in the network.
By contrast, eccentricity and closeness centrality represent a firm’s closeness, whereas betweenness centrality measures a firm’s substitutability andindicates the firm’s central role in the network. Eigenvector centrality captures the magnitude of the network relationships.
The following subsections use the definitions for each type of centrality
to analyze interconnectedness, focusing on eccentricity, closeness centrality,
betweenness centrality, and eigenvector centrality in more detail.
4.2.1. Eccentricity
Eccentricity is a measure of the maximum distance between a single firm
and any other firm in the network. The distance E(bi , bj ) between firms bi
and bj is the sum of the edge weights expressed in the shareholding exposureson the shortest path from bi to bj in network G. Thus, the eccentricity of a
firm bi isE(bi ) = arg max d(bi , bj ),
bj ∈G

18

(4)

11

0.028

10.5

0.026

10

0.024

9.5

0.022

Degree

0.03

2009

2010

2011

2012

2013

2014

3.8

1.95

3.7

1.9

3.6

1.85

3.5

1.8

3.4

1.75

3.3

1.7

3.2

1.65

3.1

1.6

3

1.55

2.9

1.5

2.8
2008

2009

2010

2011

2012

2013

2014

1.45
2015

Figure 5: direct and indirect centralities
19

x 10
1.5

-3

1.45

1.4
Closeness

Eccentricity

indirect centralities

0.02
2015

1.35

1.3

1.25

Betweenness

92008

Eigenvector

direct centralities
11.5

where E(bi ) ≥ 1. Table 4 shows that the average of this measure is approximately 3.3, with no large structural change in the network, regardless of 3.8
in FY2014.
4.2.2. Closeness centrality
Closeness centrality is the function of farness, which represents the sumof distances to all other firms. The centrality for firm i isC(bi ) =n∑d(bi , bj )/(n − 1),

(5)

j=1

where d(bi , bj ) is the number of shareholdings in the shortest path between
firms i and j, and hence, d ≥ 1.
Closeness centrality tracks the proximity of one firm to another. However,this measure does not apply to networks with disconnected components because the firms in this case have infinite distance between them. A potential
alternative allows researchers to apply the measure to disconnected component networks, and simultaneously, maintain the original theory behind themeasure. Table 4 shows farness gradually decreasing from 1.91 to 1.45 forthe period. This means that firms are more closely interconnected over time.
4.2.3. Betweenness centrality
Betweenness centrality (Krause and Giansante, 2012; Kanno, 2015a) represents the number of times a firm acts as a bridge along the shortest path
between two other firms. A firm with high betweenness centrality can potentially influence the spread of information through the network. If the normalized betweenness centrality, defined as (bc−min(bc))/(max(bc)−min(bc))(bc: the betweenness centrality of a firm), is close to one, a node (i.e., firmA) acts as a bridge along most of the shortest paths connecting two otherfirms (i.e., firms B and C). If it is close to zero, firm A is less important tofirms B and C) (Kanno, 2015a).
The betweenness centrality of firm i in a network is
B(bi ) =∑j<k;i∈{k,j}/gj,k (bi )
,
gj,k

(6)

where gj,k is the number of shortest paths between firms j and k, and gj,k (bi )is the number of shortest paths between firms j and k, with firm i acting as20

a bridge.
Table 4 shows that after FY2009, normalized centrality increases slowly
for the period. In addition, the correlations between this centrality and twoother indirect centralities (eccentricity and closeness) are both negative forthe period (lower panel of Figure 5).
4.2.4. Eigenvector centrality
Eigenvector centrality is a natural extension of the simple degree centrality. Degree centrality awards one centrality point for every network neighborof a firm. However, not all neighbors are equivalent. In many cases, afirm’s importance in a network increases owing to its connections to otherimportant firms. This defines the concept of eigenvector centrality (Newman, 2010). The advantage of eigenvector centrality over other centrality
measures is that it not only captures the number of firms linked to the target firm (degree centrality), but also the centrality of those adjacent firms.
Hence, a firm has a higher eigenvector centrality score if it is connected tomore firms with higher centrality scores.
Let C e (g) denote the eigenvector centrality associated with network g. A
firm’s centrality is∑proportional to the sum of the centrality of its neighboringfirms, λCie (g) = j gij Cje (g), for firm i. Using matrix notation,
λC e (g) = gC e (g),(7)

where λ is a proportionality factor. Thus, from Equation (7), C e (g) is aneigenvector of g and λ is its corresponding eigenvalue. As its centrality is ameasure with nonnegative values, we use the eigenvector associated with thelargest eigenvalue (Jackson, 2010).
Table 4 shows that the average eigenvector centrality for the period gradually increases with the network size. In addition, the upper panel of Figure 5
denotes the average increasing trend with time of lapse, which is quite diﬀerent from the degree centrality. In many cases, it is appropriate for firms withhigh in-degrees to have centrality even if they are not in a strongly connectedcomponent or its out-component (Newman, 2010). In response, we examine
the sums of in-degree and out-degree of the top thirty entities. Consequently,
the eigenvector centrality increases when the in-degree is higher than the outdegree in the following fiscal year. This tendency is in accordance with theupper panel of Figure 5.

21

4.2.5. Tripartite network by blockholdings
As shown in Figure 1, we analyze the tripartite network related to blockholdings. Insurers or banks hold almost no insurer stocks. Hence, we focus
on bank stocks held by insurers or banks.
Table 5 shows in-degrees expressed in numbers of bank stocks held by major blockholders. Major blockholders include eight life insurers, eight non-life
insurers including three mega-non-life insurers (Tokio Marine, Sompo Japan
Nippon Koa, and MS&AD Insurance), and seven major banks including
three mega-bank groups (Mitsubishi UFJ Financial Group, Mizuho Financial
Group (including Mizuho Trust), Sumitomo Mitsui Financial Group). Mitsui Sumitomo Insurance Group Holdings, Aioi Insurance, and Nissay Dowa
General Insurance merged into MS&AD Insurance Group Holdings in April
2010.
There is a ‘double-gearing problem’ in shareholdings of bank stocks by life
insurers. This denotes the act or practice of two or more firms pooling theirrisk by placing capital in each other. In one of the most common examplesof double gearing, a life insurer buys shares in a bank, and in exchange, thebank extends credit to the insurer.
As shown in Table 5, major life insurers hold more bank shares than
non-life insurers and banks in terms of the number of listed banks, which
is 94 (April 2008 to September 2014) to 93 (October 2014 to March 2016).
These numbers indicate that life insurers hold large numbers of bank stocks
compared to that of banks and non-life insurers. However, the number isgradually decreasing due to the sell-oﬀ of stock cross-holdings. If life insurers
use a risk-adjusted performance measure such as RORA, as shown in Table
3, a more eﬀective market discipline will work on the bank shares held bylife insurers.
4.2.6. Firm ranking by degree
Table 6 shows the ranking of the top twenty entities according to interconnectedness, measured by the degree of their nodes. They include 11–13
banks and 7–9 insurers. No firm ranks in the top twenty, although we exclude
relations between firms from our dataset.
This table includes major banks such as Mitsubishi UFJ Financial Group,
Mizuho Financial Group (including Mizuho Trust), Sumitomo Mitsui Financial Group, Resona Holdings, Sumitomo Mitsui Trust Holdings (Sumitomo
Trust prior to the merger), major regional banks such as Yokohama bank,
Hokuhoku FG, and Fukuoka FG; three mega non-life insurance groups such
22

Table 5: In-degrees expressed in the number of bank stocks held by major
blockholders
FY
Nippon Life
Sumitomo Life
Dai-ichi Life
Asahi Life
Mitsui Life
Fukoku LifeT&D HD
Meiji Yasuda Life
Total
Tokio Marine HD
Sompo HD
Nippon Koa
MS&AD Insurance Group HD
Mitsui Sumitomo Insurance
Aioi Insurance
Nissay Dowa Insurance
Nisshin Fire
Kyoei Fire
Daido Fire
Fuji Fire
Asahi Fire
Total
MUFJ FG
Mizuho FG
SMFGResona HD
Sumitomo Mitsui Trust Bank
Chuo Mitsui Trust Bank
Shinsei Bank
Aozora Bank
Total

2008 2009 2010 2011 2012 2013 2014 2015
49
49
39
38
47
46
43
40
35
32
22
16
14
14
14
11
39
40
30
31
40
39
40
35
9
9
2
2
7
5
6
5
5
4
2
2
3
4
5
3
5
5
2
2
4
3
3
3
7
7
6
6
5
5
6
4
0
0
0
0
0
0
0
0
149 146 103 97
120 116 117 101
0
0
0
0
0
0
0
0
30
32
22
21
26
25
38
33
19
19
17
16
18
19
19
18
18
14
20
21
15
14
9
9
7
9
4
4
2
2
4
4
3
4
3
3
1
1
3
3
3
2
2
2
2
2
2
2
2
2
2
2
0
0
1
1
1
1
1
1
1
1
1
0
0
0
97
102 60
57
74
72
65
56
49
43
28
28
34
34
34
28
59
51
24
22
36
32
33
31
8
10
6
6
10
9
9
9
5
4
2
1
2
2
2
2
4
4
1
1
6
6
7
6
2
2
1
1
2
1
2
2
2
2
2
1
1
1
1
1
1
1
1
1
130 116 65
62
91
86
88
78

Note: A hyphen denotes that the firm is not an extinct firm in the fiscal year. As the life insurer is amutual insurance firm, no bank or insurer holds life insurer stocks.

23

as Tokio Marine Holdings, MS&AD Insurance Group Holdings, Sompo Japan
Nipponkoa Holdings; and major life insurers such as Nippon Life, Dai-Ichi
Life Insurance, Sumitomo Life, T&D Holdings.
Because these insurers conduct portfolio investments, their degrees equal
to in-degrees. By contrast, because banks traditionally conduct cross-holdings
to maintain business relationships, their degrees comprise both in-degrees
and out-degrees. Especially, the degree centralities for the three mega-bankgroups are all over one thousand. This explains that listed firms conduct
cross-shareholdings with three mega-banks.
However, the degree centralities for the three mega-bank groups at theend of March 2016 decreased sharply from the levels at the end of March
2015 (MUFJ FG: −21.5%, Mizuho FG: −16.7%, and SMFG: −13.9%). In a
June 20, 2016 Nikkei newspaper article, these institutions declared that they
do not hold cross-holding stocks for the purpose of policy.
Figures 6 & 7 depict directed graphs based on degrees over 70 at the endof March 2009 and the end of March 2016, respectively, for a visual analysis.
The direction of the arrow is from issuer to shareholder. For example, Nippon
Life, a major institutional investor in Japan, has 955 in-degrees and 0 outdegrees in FY2009. As the edge is weighted by exposure, some thick ingoingedges flow into banks and life insurers. These graphs do not show firms.
In addition, the Mitsubishi UFJ Financial Group and Sumitomo Mitsui
Financial Group repurchased shares in FY2015. They bought back issued
shares at 100 billion yen and around 200 million yen, respectively, for premium redemption to shareholders. As its background, MUFJ FG issued
senior bonds worth USD 5 billion in March 2016 and USD 2 billion in April
2016 to meet the total loss absorbency capacity (TLAC)7 requirements for
the G-SIBs in Basel III (BCBS, 2016). SMFG also issued senior bonds worth
EUR 1.5 billion in June 2016 and USD 4.5 billion in July 2016.
Figure 8 shows the six transition panels of a directed graph based on degrees over 70 for FY2009–2014. The graphs do not show any firms, although

7

In November 2015, the Financial Stability Board issued the final TLAC standard
for G-SIBs. The TLAC standard defines a minimum requirement for the instruments
and liabilities that should be readily available to absorb losses in resolution. Under the
standard, each G-SIB is required to hold TLAC debt in an amount not less than 16%
of its risk-weighted assets and 6% of the applicable Basel III leverage ratio denominator
by January 1, 2019, and not less than 18% of its risk-weighted assets and 6.75% of theapplicable Basel III leverage ratio denominator by January 1, 2022.

24the dataset has the largest number of firms in the tripartite network.
4.2.7. Firm ranking by betweenness centralities
Table 7 lists the top twenty entities according to interconnectedness, measured in terms of the betweenness centrality of their nodes. Compared toTable 6, the number of insurers is 2–3. Instead, the number of major regional banks increased to 11–13. By contrast, few or no firms are shownin this table. For example, Nippon Life, which has high degrees, does notrank even in the top fifty. Issuers with shares held by many entities tend
to have higher betweenness centrality, whereas life insurers conduct portfolio
investments as unilateral shareholdings, not cross-holdings. As mega-bankgroups have a strong presence in maintaining business relationships throughcross-shareholdings, they play an important role in Japan’s stock market.
5. Stress test
We conduct a stress test to verify the rise of credit risk exposures byshocks in the cross-shareholding network at an evaluation point in the future.
Our test diﬀers slightly from typical macro stress tests, which consider theshocks of macroeconomic variables on risk parameters for each entity (Henry
and Kok, 2013; Kanno, 2015a, 2015b). In contrast, our test considers the riseof credit risk exposures by giving an exogenous shock under severely adverse
economic condition.
According to R&I (2016), the empirical PDs pertaining to Japanese firms
rated as speculative grades (BB category or lower) by R&I reached a peak of
15% during the Heisei great recession (1997-1998) and just after the global
financial crisis (2009). Hence, because the maximum of model-based PDs atthe end of March 2016 is 5%, we added 5%, 10%, and 15% evenly to theoriginal probability of default of each entity. The evaluation time point isassumed to be the end of March 2018. The other parameters (e.g., LGD andEAD) are assumed to be the same as those at the end of March 2016. Hence,
interconnectedness as measured in terms of degree centrality or betweenness
centrality is assumed to be the same as that at the end of March 2016. However, interconnectedness as measured in terms of weighted-degree8 changes
depending on the credit risk exposures as weights.
8

The weighted-degree of a node is calculated as the sum of the weights of edges for all
its incoming and outgoing nodes.

25

26

T&D HOLDINGS(124)
NISHI-NIPPON
CITY
BANK
(122)

19

13

HIROSHIMABANK (127)
T&D HOLDINGS(127)

TOKIO MARINEHOLDINGS (170)
JOYO
BANK
(150)
SOMPO JAPANNIPPONKOA(149)
NOMURAHOLDINGS(147)
SENSHU IKEDAHOLDINGS (146)
T&D HOLDINGS(146)

SUMITOMOMITSUI TRUST(725)
RESONA HOLDINGS (587)
MS&AD INSURANCE (444)
MIZUHO TRUST(378)
SUMITOMOLIFE (290)
SOMPO JAPAN(244)
YOKOHAMABANK (221)
HOKUHOKU FG
(188)
AIOI
NISSAY
DOWA INS (171)
LIFE

AIOI
NISSAY
DOWA INS (167)
T&D HOLDINGS(159)
SOMPO JAPANNIPPONKOA(157)
NOMURAHOLDINGS(155)
SENSHU IKEDAHOLDINGS (154)
JOYO
BANK
(147)

RESONA HOLDINGS (600)
MS&AD INSURANCE (453)
SUMITOMOLIFE (288)
SOMPO JAPAN(231)
YOKOHAMABANK (220)
HOKUHOKU FG(187)
MIZUHO TRUST(177)
TOKIO MARINEHOLDINGS (177)NIPPON(753)

2011
MITSUBISHI
UFJ FG (2052)
MIZUHO
FG(1655)
SMFG (1289)
SUMITOMOMITSUI TRUST(830)
DAI-ICHI
LIFE
(775)

Note: Figures in parentheses indicate degree centrality in the thousands.

20

17

15

14

BANK

JOYO
(129)

18

11

12

JOYO
(128)

YAMAGUCHI
FG (144)AIOINISSAY
DOWA INS (142)
FUKUOKA
FG
(128)

AIOI
NISSAY
DOWA INS (164)
YAMAGUCHI
FG (142)FUKUOKA
FG
(129)

16

9

10

BANK

RESONA HOLDINGS (573)
MS&AD INSURANCE (411)
SUMITOMOLIFE (377)
MIZUHO TRUST(315)
SOMPO JAPAN(304)
YOKOHAMABANK (243)
HOKUHOKU FG(192)
NIPPONKOA INSURANCE (165)

RESONA HOLDINGS (577)
MS&AD INSURANCE (417)
SOMPO JAPAN(376)
SUMITOMOLIFE (374)
MIZUHO TRUST(320)
YOKOHAMABANK (236)
NIPPONKOA INSURANCE (196)
HOKUHOKU FG
(184)

7

8

6

LIFE

NIPPON(770)

SUMITOMOMITSUI TRUST(654)
DAI-ICHI
LIFE
(593)

SUMITOMOMITSUI TRUST(636)
DAI-ICHI
LIFE
(612)

5

3
4

2

2010
MITSUBISHI
UFJ FG (1994)
MIZUHO
FG(1535)
SMFG (1212)
DAI-ICHI
LIFE
(784)

2009
MITSUBISHI
UFJ FG (2075)
MIZUHO
FG(1591)
SMFG (1139)
NIPPON
LIFE
(952)

2008
MITSUBISHI
UFJ FG (2075)
MIZUHO
FG(1595)
SMFG (1135)
NIPPON
LIFE(955)

Ranking
1

LIFE

TOKIO MARINEHOLDINGS (178)
T&D HOLDINGS(173)
NOMURAHOLDINGS(160)
SOMPO JAPANNIPPONKOA
(157)
JOYO
BANK
(155)
YAMAGUCHI
FG (154)RESONA HOLDINGS (643)
MS&AD INSURANCE (505)
SUMITOMOLIFE (332)
SOMPO JAPAN(262)
YOKOHAMABANK (243)
HOKUHOKU FG(212)
MIZUHO TRUST
(211)
AIOI
NISSAY
DOWA INS (194)DAI-ICHI
(839)

2012
MITSUBISHI
UFJ FG (2191)
MIZUHO
FG(1795)
SMFG (1400)
SUMITOMOMITSUI TRUST(969)
NIPPON
LIFE
(883)
LIFE

FUKUOKAFG(148)
YAMAGUCHI
FG (143)

TOKIO MARINEHOLDINGS (176)
T&D HOLDINGS(167)
SOMPO JAPANNIPPONKOA
(155)
JOYO
BANK
(149)

RESONA HOLDINGS (624)
MS&AD INSURANCE (493)
SUMITOMOLIFE (311)
SOMPO JAPAN(234)
YOKOHAMABANK (228)
HOKUHOKU FG(211)
MIZUHO TRUST
(208)
AIOI
NISSAY
DOWA INS (181)DAI-ICHI
(813)

2013
MITSUBISHI
UFJ FG (2138)
MIZUHO
FG(1717)
SMFG (1365)
SUMITOMOMITSUI TRUST(936)
NIPPON
LIFE
(826)
LIFE

JOYO
BANK
(144)
FUKUOKA
FG
(142)

AIOI
NISSAY
DOWA INS (170)
T&D HOLDINGS(158)
SOMPO JAPANNIPPONKOA(155)
YAMAGUCHI
FG (147)RESONA HOLDINGS (605)
MS&AD INSURANCE (476)
SOMPO JAPAN(329)
SUMITOMOLIFE (282)
YOKOHAMABANK (208)
HOKUHOKU FG(199)
MIZUHO TRUST(199)
TOKIO MARINEHOLDINGS (180)NIPPON(730)

2014
MITSUBISHI
UFJ FG (2056)
MIZUHO
FG(1639)
SMFG (1316)
SUMITOMOMITSUI TRUST(901)
DAI-ICHI
LIFE
(753)

Table 6: Top 20 companies ranked by interconnectedness: degree centrality

LIFE

FG

HIROSHIMABANK (122)
CHIBA
BANK
(122)

FUKUOKA(130)

RESONA HOLDINGS (529)
MS&AD INSURANCE (413)
SOMPO JAPAN(258)
SUMITOMOLIFE (249)
HOKUHOKU FG(191)
TOKIO MARINEHOLDINGS (175)
MIZUHO TRUST(161)
SOMPO JAPANNIPPONKOA(151)
T&D HOLDINGS(143)
YAMAGUCHI
FG (138)AIOINISSAY
DOWA INS (136)

NIPPON(622)

2015
MITSUBISHI
UFJ FG (1828)
MIZUHO
FG(1491)
SMFG (1182)
SUMITOMOMITSUI TRUST(806)
DAI-ICHI
LIFE
(685)

27

HOKUHOKU FG(0.055)MIZUHO TRUST(0.045)
SOMPO JAPAN(0.041)

YOKOHAMABANK (0.035)

NISHI-NIPPON
CITY
BANK
(0.031)YAMAGUCHI
FG (0.024)
HIROSHIMABANK (0.023)
SHIZUOKABANK (0.021)
T&D HOLDINGS(0.021)
HACHIJUNIBANK (0.02)
MINAMINIPPON BANK
(0.019)CHUGOKUBANK (0.017)
IYO
BANK(0.016)FUKUOKA(0.016)

7

10

11

20

DAISHI(0.015)BANK

T&D HOLDINGS(0.016)BANK
OFNAGOYA (0.015)

HIROSHIMABANK (0.024)
SHIZUOKABANK (0.023)
HACHIJUNIBANK (0.021)
FUKUOKAFG(0.018)BANK OF KYOTO (0.018)
CHUGOKUBANK (0.016)

NISHI-NIPPON
CITY
BANK
(0.031)YAMAGUCHI
FG (0.025)

HOKUHOKU FG(0.057)
MIZUHO TRUST(0.044)
YOKOHAMABANK (0.036)

MS&AD INSURANCE (0.059)

2009
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.657)
SMFG (0.343)
SUMITOMOMITSUI TRUST(0.133)RESONA HOLDINGS (0.128)

JOYO(0.016)

BANK

IYO
BANK
(0.017)
CHUGOKUBANK (0.016)

HACHIJUNIBANK (0.027)
FUKUOKAFG(0.026)SHIZUOKABANK (0.026)
HIROSHIMABANK (0.023)
HOKKOKUBANK (0.017)
BANK OF KYOTO (0.017)

NISHI-NIPPON
CITY
BANK
(0.03)
YOKOHAMABANK (0.029)

SUMITOMOMITSUI TRUST(0.129)
MS&AD INSURANCE (0.081)
MIZUHO TRUST(0.06)
HOKUHOKU FG(0.048)RESONA HOLDINGS (0.14)

2010
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.609)SMFG (0.366)
DAI-ICHI
LIFE(0.248)DAISHI(0.015)BANKBANK OF KYOTO (0.016)
CHUGOKUBANK (0.015)

HACHIJUNIBANK (0.025)
FUKUOKAFG(0.023)
HIROSHIMABANK (0.023)
CHIBA
BANK
(0.018)
HOKKOKUBANK (0.017)
IYO
BANK(0.016)

YOKOHAMABANK (0.028)

MS&AD INSURANCE (0.088)
HOKUHOKU FG(0.045)NISHI-NIPPON
CITY
BANK
(0.032)SHIZUOKABANK (0.029)

SUMITOMOMITSUI TRUST(0.156)RESONA HOLDINGS (0.142)

2011
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.662)
SMFG (0.394)
DAI-ICHI
LIFE(0.235)

Note: Figures in parentheses indicate betweenness centrality in the thousands.

19

18

17

16

15

14

13

12

9

8

FG

MS&AD INSURANCE (0.056)

6

5

3
4

2

2008
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.649)SMFG (0.333)
SUMITOMOMITSUI TRUST(0.129)RESONA HOLDINGS (0.126)

Ranking
1

CHUGOKUBANK (0.017)
SENSHU IKEDAHOLDINGS(0.016)YAMAGUCHI
FG (0.015)

HACHIJUNIBANK (0.023)
FUKUOKAFG(0.023)
HIROSHIMABANK (0.021)
T&D HOLDINGS(0.019)DAISHI
BANK(0.018)CHIBA
BANK(0.018)NISHI-NIPPON
CITY
BANK
(0.027)SHIZUOKABANK (0.027)

MS&AD INSURANCE (0.083)
HOKUHOKU FG(0.049)
YOKOHAMABANK (0.031)

SUMITOMOMITSUI TRUST(0.174)RESONA HOLDINGS (0.135)

2012
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.689)SMFG (0.403)
DAI-ICHI
LIFE(0.29)CHUGOKUBANK (0.017)

IYO
BANK
(0.017)
HOKKOKUBANK (0.017)

FUKUOKAFG(0.026)HACHIJUNIBANK (0.023)
HIROSHIMABANK (0.023)
CHIBA
BANK
(0.019)T&D HOLDINGS(0.019)DAISHI
BANK(0.017)NISHI-NIPPON
CITY
BANK
(0.029)
YOKOHAMABANK (0.026)

MS&AD INSURANCE (0.083)
HOKUHOKU FG(0.05)SHIZUOKABANK (0.031)

SUMITOMOMITSUI TRUST(0.174)RESONA HOLDINGS (0.136)

2013
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.662)
SMFG (0.403)
DAI-ICHI
LIFE(0.279)

BANK

BANK

T&D HOLDINGS(0.016)IYO(0.017)DAISHI(0.016)

YOKOHAMABANK (0.024)
HIROSHIMABANK (0.024)
HACHIJUNIBANK (0.023)
CHIBA
BANK
(0.019)CHUGOKUBANK (0.019)
BANK
OF
NAGOYA (0.017)

NISHI-NIPPON
CITY
BANK
(0.03)FUKUOKAFG(0.025)

MS&AD INSURANCE (0.089)
HOKUHOKU FG(0.054)SHIZUOKABANK (0.032)

SUMITOMOMITSUI TRUST(0.177)RESONA HOLDINGS (0.14)

2014
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.647)
SMFG (0.386)
DAI-ICHI
LIFE(0.255)

Table 7: Top 20 companies according to interconnectedness: betweenness centrality

FG

BANK
OF
NAGOYA (0.015)

CHIBA
BANK
(0.016)
HOKKOKUBANK (0.015)

HIROSHIMABANK (0.027)
HACHIJUNIBANK (0.026)
CHUGOKUBANK (0.019)
IYO
BANK(0.019)YAMAGUCHI
FG (0.018)
T&D HOLDINGS(0.016)FUKUOKA(0.029)

MS&AD INSURANCE (0.084)
HOKUHOKU FG(0.065)NISHI-NIPPON
CITY
BANK
(0.036)SHIZUOKABANK (0.03)

SUMITOMOMITSUI TRUST(0.187)RESONA HOLDINGS (0.135)

2015
MITSUBISHI
UFJ FG (1)MIZUHOFG(0.687)
SMFG (0.424)
DAI-ICHI
LIFE(0.225)JUROKU BANKTOKIO MARINE HOLDINGSBANK OF KYOTO
T&D HOLDINGSMITSUI LIFE
SOMPO JAPANOGAKI KYORITSU BANKAICHI BANKCHUGOKU BANKHACHIJUNI BANKHIROSHIMA BANKNORINCHUKIN BANKMITSUBISHI UFJ FG

MIZUHO FG
SHIZUOKA BANKSUMITOMO MITSUI TRUSTHYAKUJUSHI BANKSHIGA BANKSENSHU/IKEDA BANKNIPPON LIFE

IYO BANKBANK OF NAGOYASMFGFUKUOKA FG
NIPPONKOA INSURANCEAIOI NISSAY DOWA INS
MS&AD INSURANCEASAHI LIFE

DAI-ICHI LIFENISHI-NIPPON CITY BANKYAMAGUCHI FG
HOKUHOKU FG

RESONA HOLDINGSSUMITOMO LIFE

NOMURA HOLDINGSJOYO BANKCHIBA BANKGUNMA BANKHOKKOKU BANKMIZUHO TRUSTMINATO BANKAIOI NISSAY DOWA INS

JAPAN TRUSTEE SERVICES BANKYOKOHAMA BANKSHINKIN BANK

Figure 6: Directed graph of degrees over 70, end of March 2009 (just after
the bankruptcy of Lehman Brothers)
Note: These graphs are drawn in the Fruchterman-Reingold layout.

28

RESONA HOLDINGSHYAKUJUSHI BANKNOMURA HOLDINGSSUMITOMO MITSUI TRUSTAIOI NISSAY DOWA INS
DAI-ICHI LIFEAICHI BANKYAMAGUCHI FG

BANK OF KYOTODAIWA SECURITIES GROUPSHIZUOKA BANKHACHIJUNI BANKSENSHU IKEDA HOLDINGSMINATO BANK

MS&AD INSURANCETOKIO MARINE HOLDINGSCHIBA BANKMIZUHO FGNIPPON LIFEBANK OF NAGOYASOMPO JAPAN NIPPONKOA

MITSUBISHI UFJ FG

HOKUHOKU FG
FUKUOKA FG
SHIGA BANKSUMITOMO LIFE

SOMPO JAPANIYO BANKNISHI-NIPPON CITY BANKJUROKU BANKNORINCHUKIN BANKJAPAN TRUSTEE SERVICES BANKOGAKI KYORITSU BANKSMFGHIROSHIMA BANKMIZUHO TRUSTHOKKOKU BANK

T&D HOLDINGSCHUGOKU BANKGUNMA BANKJOYO BANK

Figure 7: Directed graph of degrees over 70, end of March 2016 (after the
implementation of Japan’s Corporate Governance Code)Note: These graphs are drawn in the Fruchterman-Reingold layout. A ring on the margin of a circle
denotes self-repurchase.

29

NOMURA HOLDINGSSENSHU IKEDA HOLDINGSRESONA HOLDINGS

T&D HOLDINGSJAPAN TRUSTEE SERVICES BANK

T&D HOLDINGSSMFGMIZUHO TRUSTAICHI BANKASAHI LIFE

OGAKI KYORITSU BANKJUROKU BANKJOYO BANKHIROSHIMA BANKHOKKOKU BANKBANK OF NAGOYAGUNMA BANKCHUGOKU BANKTOKIO MARINE HOLDINGSAIOI NISSAY DOWA INSNORINCHUKIN BANKJOYO BANKBANK OF NAGOYASUMITOMO LIFE

IYO BANKSHINKIN BANK
MS&AD INSURANCERESONA HOLDINGSAICHI BANKIYO BANK

MS&AD INSURANCEOGAKI KYORITSU BANKHACHIJUNI BANKSMFGMIZUHO FGTOKIO MARINE HOLDINGSMINATO BANKHOKKOKU BANKHIROSHIMA BANKNIPPON LIFE
HOKUHOKU FG

FUKUOKA FG
NISHI-NIPPON CITY BANKSOMPO JAPANMITSUBISHI UFJ FG

MITSUBISHI UFJ FG
GUNMA BANKDAI-ICHI LIFENIPPONKOA INSURANCESUMITOMO MITSUI TRUSTSOMPO JAPAN NIPPONKOA

CHIBA BANKDAIWA SECURITIES GROUPDAI-ICHI LIFECHIBA BANKSOMPO JAPANMIZUHO FG

SUMITOMO LIFENIPPONKOA INSURANCEYAMAGUCHI FG

NIPPON LIFEMIZUHO TRUSTSHIGA BANKBANK OF KYOTOJUROKU BANKCHUGOKU BANKNORINCHUKIN BANKSUMITOMO MITSUI TRUSTSHIZUOKA BANKAIOI NISSAY DOWA INS

SHIZUOKA BANKNISHI-NIPPON CITY BANKSHIGA BANKHOKUHOKU FGAIOI NISSAY DOWA INS

YAMAGUCHI FGFUKUOKA FG

YOKOHAMA BANKHYAKUJUSHI BANKBANK OF KYOTO

YOKOHAMA BANKHACHIJUNI BANKNOMURA HOLDINGSJAPAN TRUSTEE SERVICES BANKHYAKUJUSHI BANKSOMPO JAPAN NIPPONKOA
SUMITOMO MITSUI TRUSTTOKIO MARINE HOLDINGS

T&D HOLDINGSNOMURA HOLDINGSDAISHI BANKMIZUHO TRUSTSENSHU IKEDA HOLDINGSJUROKU BANKHYAKUJUSHI BANKHOKKOKU BANKTOKIO MARINE HOLDINGSJUROKU BANKBANK OF NAGOYARESONA HOLDINGSSHIGA BANKMINATO BANKHIROSHIMA BANKYAMAGUCHI FG

JOYO BANKGUNMA BANKSHIGA BANKHOKKOKU BANKNANTO BANKMIZUHO FG

MS&AD INSURANCEDAIWA SECURITIES GROUPNIPPONKOA INSURANCEHIROSHIMA BANKNIPPONKOA INSURANCEGUNMA BANKFUKUOKA FG

HOKUHOKU FG

T&D HOLDINGSMITSUBISHI UFJ FGHYAKUJUSHI BANKNIPPON LIFE
MS&AD INSURANCEMITSUBISHI UFJ FG

SOMPO JAPAN NIPPONKOADAI-ICHI LIFESENSHU IKEDA HOLDINGSBANK OF KYOTO

SMFG

IYO BANKIYO BANKSHIZUOKA BANKSMFGAIOI NISSAY DOWA INSSUMITOMO LIFENIPPON LIFE
HACHIJUNI BANKSUMITOMO LIFEMIZUHO FG

CHUGOKU BANKNORINCHUKIN BANKTOKAI TOKYO FINANCIALNISHI-NIPPON CITY BANKOGAKI KYORITSU BANKFUKUOKA FG

NANTO BANKCHIBA BANKRESONA HOLDINGSYOKOHAMA BANKDAI-ICHI LIFE

SUMITOMO MITSUI TRUSTNISHI-NIPPON CITY BANKJAPAN TRUSTEE SERVICES BANKCHUGOKU BANKJOYO BANKAICHI BANKHOKUHOKU FG

OGAKI KYORITSU BANKMINATO BANKSOMPO JAPANMIZUHO TRUSTBANK OF NAGOYAAIOI NISSAY DOWA INS

CHIBA BANKAICHI BANKSOMPO JAPANYAMAGUCHI FG

HACHIJUNI BANKBANK OF KYOTODAIWA SECURITIES GROUPSHIZUOKA BANKYOKOHAMA BANKNOMURA HOLDINGSNORINCHUKIN BANKSHINKIN BANKHOKUHOKU FG
HOKKOKU BANKAIOI NISSAY DOWA INS
JAPAN TRUSTEE SERVICES BANKDAIWA SECURITIES GROUPBANK OF KYOTO
AIOI NISSAY DOWA INS

IYO BANKCHUGOKU BANKFUKUOKA FG

TOKIO MARINE HOLDINGSDAISHI BANKDAISHI BANKNISHI-NIPPON CITY BANKCHUGOKU BANKGUNMA BANKMINATO BANKNORINCHUKIN BANKHYAKUJUSHI BANKHACHIJUNI BANKNIPPONKOA INSURANCEOGAKI KYORITSU BANKSHIGA BANKJUROKU BANK
MS&AD INSURANCESMFG

SOMPO JAPANMIZUHO TRUSTMIZUHO FG
NOMURA HOLDINGSBANK OF NAGOYASUMITOMO LIFEHIROSHIMA BANKCHIBA BANKNIPPON LIFE
HOKUHOKU FG

T&D HOLDINGSSMFGSOMPO JAPAN

MS&AD INSURANCEDAI-ICHI LIFE

JOYO BANKSUMITOMO LIFENORINCHUKIN BANKSUMITOMO MITSUI TRUSTJOYO BANKRESONA HOLDINGSMITSUBISHI UFJ FG

SHIZUOKA BANKNIPPON LIFE

DAI-ICHI LIFEYAMAGUCHI FG

MITSUBISHI UFJ FG

HACHIJUNI BANKTOKIO MARINE HOLDINGSIYO BANKMINATO BANKNISHI-NIPPON CITY BANKHOKKOKU BANKFUKUOKA FG
HIROSHIMA BANKSUMITOMO MITSUI TRUSTBANK OF NAGOYABANK OF KYOTO

NOMURA HOLDINGSAICHI BANKSOMPO JAPAN NIPPONKOAMIZUHO FGDAIWA SECURITIES GROUPCHIBA BANKHYAKUJUSHI BANKAICHI BANKYOKOHAMA BANKGUNMA BANKSHIZUOKA BANKMIZUHO TRUSTRESONA HOLDINGSJUROKU BANKSENSHU IKEDA HOLDINGS
T&D HOLDINGSSOMPO JAPAN NIPPONKOA

JAPAN TRUSTEE SERVICES BANKYOKOHAMA BANKYAMAGUCHI FG
OGAKI KYORITSU BANKSENSHU IKEDA HOLDINGSSHINKIN BANKSHIGA BANK

Figure 8: Directed graphs of degrees over 70
Note: The six panels show directed graphs of firm nodes over 70 degrees at the end of March 2010 andMarch 2011 from the upper-left panel to the upper-right panel; at the end of March 2012 andMarch 2013 from the middle-left panel to the middle-right panel; and at the end of March 2014
and March 2015 from the lower-left panel to the lower-right panel.

30

Table 8: Change in bilateral credit risk-weighted assets

Blockholdings
Policy ownership securities
Portfolio investments
Deemed securities
Total amount

BS+5%
218%
90%
85%
90%
191%

BS+10%
292%
132%
126%
131%
258%

BS+15%
339%
158%
151%
157%
301%

Note: Abbreviation: BS: Baseline scenario of PD. Each change indicates the growthrate from the baseline level before adding stress.

Table 8 indicates the growth rate of credit risk exposures by holding type.
The growth rate of 258% is extremely large for the scenario of BS+10%9
experienced for two financial crises aﬀecting the Japanese market. Especially,
its rate is approximately three hundred percent in terms of blockholdings. As
shown in Figure 9, because any stress is not loaded to the network structure,
its structure remains unchanged. However, owing to the diﬀerence in riseof credit risk exposures, Mitsubishi UFJ Financial Group and Nippon Life
Insurance, which are the top two institutions, are in reverse order in loading
stresses of 5%, 10% and 15% in terms of weighted-degree, whereas the othersranked lower than the two institutions remain almost unchanged.

9‘BS’ (Baseline Scenario) in Table 8 indicates the PDs at the end of March 2016 relating
to all insurers, banks, and firms.

31

BANK OF KYOTOTOKIO MARINE HOLDINGSDAIWA SECURITIES GROUPHACHIJUNI BANK
T&D HOLDINGSOGAKI KYORITSU BANKSENSHU IKEDA HOLDINGSSUMITOMO MITSUI TRUSTTOKIO MARINE HOLDINGSBANK OF KYOTOJUROKU BANKCHUGOKU BANKCHIBA BANKAIOI NISSAY DOWA INS

JOYO BANKDAI-ICHI LIFE

GUNMA BANKYAMAGUCHI FG

SOMPO JAPAN NIPPONKOAHYAKUJUSHI BANKAICHI BANKMINATO BANK
MS&AD INSURANCE

MS&AD INSURANCEIYO BANKDAIWA SECURITIES GROUPSOMPO JAPAN NIPPONKOABANK OF NAGOYAHOKKOKU BANKAICHI BANKJAPAN TRUSTEE SERVICES BANKSHIGA BANKOGAKI KYORITSU BANKFUKUOKA FG
IYO BANKMIZUHO FGHOKKOKU BANKSHIZUOKA BANKSOMPO JAPANMIZUHO FG

MITSUBISHI UFJ FG

NOMURA HOLDINGSNIPPON LIFE

MITSUBISHI UFJ FG
JOYO BANKMINATO BANKSOMPO JAPANNIPPON LIFESENSHU IKEDA HOLDINGSSHIZUOKA BANKNISHI-NIPPON CITY BANKSUMITOMO LIFESMFGYAMAGUCHI FG

HYAKUJUSHI BANKMIZUHO TRUSTAIOI NISSAY DOWA INSSMFGSUMITOMO MITSUI TRUSTNISHI-NIPPON CITY BANKHIROSHIMA BANKBANK OF NAGOYASUMITOMO LIFESHIGA BANKDAI-ICHI LIFE

HOKUHOKU FG
JAPAN TRUSTEE SERVICES BANKNORINCHUKIN BANKCHIBA BANKHIROSHIMA BANKCHUGOKU BANKFUKUOKA FG

NORINCHUKIN BANKHACHIJUNI BANKMIZUHO TRUSTRESONA HOLDINGSGUNMA BANKNOMURA HOLDINGSJUROKU BANKHOKUHOKU FG
CHIBA BANK

T&D HOLDINGSHYAKUJUSHI BANKRESONA HOLDINGSTOKIO MARINE HOLDINGSSOMPO JAPAN NIPPONKOA

OGAKI KYORITSU BANKSUMITOMO MITSUI TRUSTJOYO BANKTOKIO MARINE HOLDINGSDAI-ICHI LIFESHIGA BANKRESONA HOLDINGSFUKUOKA FG

GUNMA BANKHOKUHOKU FGSHIZUOKA BANKFUKUOKA FG

NORINCHUKIN BANKSHIZUOKA BANKRESONA HOLDINGSHIROSHIMA BANKSOMPO JAPANSOMPO JAPAN NIPPONKOABANK OF KYOTO
MS&AD INSURANCEDAIWA SECURITIES GROUPHOKKOKU BANKSOMPO JAPAN

MS&AD INSURANCENISHI-NIPPON CITY BANKSUMITOMO MITSUI TRUSTCHUGOKU BANKHYAKUJUSHI BANKHACHIJUNI BANKNIPPON LIFE

SUMITOMO LIFENORINCHUKIN BANKMIZUHO FGNISHI-NIPPON CITY BANKNIPPON LIFE
JAPAN TRUSTEE SERVICES BANKMITSUBISHI UFJ FGAICHI BANKMIZUHO FGAICHI BANKJOYO BANKJUROKU BANKMITSUBISHI UFJ FG

NOMURA HOLDINGSBANK OF NAGOYANOMURA HOLDINGSHIROSHIMA BANKDAI-ICHI LIFE

SUMITOMO LIFECHIBA BANKHOKKOKU BANKIYO BANKMIZUHO TRUSTMINATO BANKIYO BANKT&D HOLDINGSMINATO BANKHACHIJUNI BANKGUNMA BANKBANK OF NAGOYASMFGSHIGA BANKYAMAGUCHI FGSMFGAIOI NISSAY DOWA INS
YAMAGUCHI FG

BANK OF KYOTODAIWA SECURITIES GROUP

AIOI NISSAY DOWA INS
T&D HOLDINGSCHUGOKU BANKJUROKU BANKSENSHU IKEDA HOLDINGSMIZUHO TRUSTOGAKI KYORITSU BANKJAPAN TRUSTEE SERVICES BANK HOKUHOKU FG

SENSHU IKEDA HOLDINGS

Figure 9: Directed graphs of weighted-degrees over 70 after adding stress,
end of March 2018
Note: The four panels show directed graphs of firm nodes over 70 degrees at base and BS+5% from theupper-left panel to the upper-right panel, and at BS+10% and BS+15% from the lower-leftpanel to the lower-right panel.

32

6. Conclusions
This study contributes to the literature by analyzing credit risk exposures
in Japan’s stock market.
First, in corporate credit risk management, we measured credit risk exposures in cross-shareholdings. Following the PD/LGD approach, credit risk
exposures become much larger than by the transitional risk weight method.
In addition, the outstanding credit risk exposures for mega-bank groups andmajor insurers substantially reduce just after the global financial crisis andslightly decrease after the implementation of JCGC. By contrast, the outstanding cross-holdings of stocks of insurers and banks by firms were almost
unchanged. The analysis results show that firms take higher risks and obtain lower returns than insurers and banks in exchange for a good business
relationship with banks.
Second, we calculated the RORA. In response to regulatory requirements,
major Japanese banks recently adopted a performance measure based on the
RORA. Compared to the Basel III, the solvency margin standard is not
restrictive. Nevertheless, insurers have an incentive to adopt performancemeasures such as the RORA in future.
Third, we analyzed the tripartite network structure of cross-shareholdings
among insurers, banks, and firms in Japan’s stock market using major centrality measures. Insurers and banks are central to the network in degree
centrality. By contrast, banks play a central role in betweenness centrality. In addition, the implementation of JCGC had only a limited eﬀect inreducing shares held via policy ownership.
Fourth, in terms of holdings of bank stocks by life insurers, we analyzed
a double-gearing problem. Major life insurers hold more bank shares than
non-life insurers and banks in terms of the number of listed banks. In-degrees
relating to bank stocks held by life insurers are large compared to that ofbanks and non-life insurers. However, the in-degrees are gradually decreasing
owing to the sell-oﬀ of stock cross-holdings. If life insurers use a risk-adjusted
performance measure such as RORA, a more eﬀective market discipline will
work on their bank shareholdings.
Fifth, by conducting stress test in terms of PDs, the shocks experienced in
the past financial crises increase extreme large credit risk exposures in termsof shareholdings. Especially, because blockholders are aﬀected substantially,
they need to monitor their own credit risk exposures.
Finally, our credit risk analysis and network analysis can warn entities
33

about credit risk management in their cross-shareholdings.
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