Analysing Systemic Risk in the Chinese Banking System

Document

Cited in

Published date	01 May 2019
Date	01 May 2019
DOI	http://doi.org/10.1111/1468-0106.12212

ANALYSING SYSTEMIC RISK IN THE CHINESE BANKING

SYSTEM

QIUBIN HUANG*University of Groningen

JAKOB DEHAAN University of Groningen, De Nederlandsche Bank and CESifo

BERT SCHOLTENS University of Groningen and University of Saint Andrews

Abstract. We examine systemic risk in the Chinese banking system by estimating the conditional

value at risk (CoVaR), the marginal expected shortfall (MES), the systemic impact index (SII) and

the vulnerability index (VI) for 16 listed banks in China for the 2007–2014 period. We f‌ind that these

measures show different patterns, capturing different aspects of systemic risk of Chinese banks. How-

ever, rankings of banks based on these measures are signif‌icantly correlated. The time-series results

for the CoVaR and MES measures suggest that systemic risk in the Chinese banking system de-

creased after the global f‌inancial crisis but started rising in 2014.

1. INTRODUCTION

Macro-prudential regulation, which aims to reduce systemic risk and achieve f‌i-

nancial stability, has been one of the most important policy innovations following

the global f‌inancial crisis (Kim and Chey, 2010; Blinder et al., 2016). However, to

implement such regulation, policy-makers need to identify systemic risk in the

banking system. This paper analyses systemic risk in the Chinese banking system.

China has achieved remarkable progress in reforming its banking system. Cur-

rently, there are 117 Chinese banks in the 2015 Top 1000 World Banks ranking1;

three of them (the Bank of China, the Industrial and Commercial Bank of China

and the Agricultural Bank of China)2are rated as global systemically important

banks. Chinese banks made $292 billion in aggregate pretax prof‌it in 2013, or

32% of total earnings of the world’s top 1000 banks, outperforming US banks (with

a share of 20%), according to The Banker magazine.3However, the Chinese bank-

ing system faces numerous challenges. Economic growth in China has been slowing

down since the global f‌inancial crisis and its export-led growth path does not seem

sustainable (Aizenman, 2015). Overcapacity in some sectors is becoming

*Address for Correspondence: Faculty of Economics and Business, University of Groningen, PO Box

800, 9700 AV Groningen, the Netherlands. E-mail: q.huang@rug.nl. We thank the discussants and

participants at the 2015 SOM PhD conference in Groningen, 2015 SYRTO conference in

Amsterdam, and 7th International IFABS conference 2015 in Hangzhou for their comments. We

are grateful for comments from the two anonymous referees. Huang thanks Chen Zhou and Yang

Jiang for their assistance on the estimations of measures adopted in this paper. All mistakes are

ours. The views expressed do not necessarily ref‌lect the views of De Nederlandsche Bank or the

Eurosystem.

1See report published on 29 June 2015 in The Banker, available from http://www.thebanker.com/

Top-1000-World-Banks/Top-1000-World-Banks-China-s-banks-show-no-signs-of-slowdown.

2See the 2014 update of the list of global systemically important banks (G-SIB), 6 November 2014,

available from: http://www.f‌inancialstabilityboard.org/2014/11/2014-update-of-list-of-global-sys-

temically-important-banks/.

3See http://www.reuters.com/article/2014/06/29/us-banks-rankings-china-idUSKBN0F411520140629.

Pacif‌ic Economic Review,••:•• (2017)

doi: 10.1111/1468-0106.12212

increasingly serious, and there seems to be a bubble in the real estate market, whose

f‌inancing mainly depends on banking loans. These challenges may affect the stabil-

ity of the banking system.4Furthermore, the rapid expansion of China’s

shadow-banking sector may pose a threat to banking stability (Li, 2014), as il-

lustrated by the default (or near-default) of several trusts exposed to the

coalmining sector in 2014.5Banks are not immune to the risks of the

shadow-banking sector, as many of them distribute wealth management prod-

ucts or ref‌inance trust companies.

A banking crisis in China would create enormous problems not only in

China but also in other countries (see Feldkircher and Korhonen (2014)

and Qiu and Zhan (2016) for evidence on China’s increasing inf‌luence on

the global economy). Therefore, it seems wise to nip the risk in the bud.

For this we need to analyse systemic risk objectively and accurately. Accord-

ing to off‌icial reports, the ratio of non-performing loans is approximately 1%

for the vast majority of banks, indicating a good health of the banking

system. However, China’s off‌icial f‌igures are often of questionable reliability,

as argued by Krugman (2011). Therefore, our research resorts to market

data, providing a more objective analysis of the soundness of the Chinese

banking system.

We investigate systemic risk via several measures. More specif‌ically, we apply

the conditional value at risk (CoVaR) measure of Adrian and Brunnermeier

(2016), the marginal expected shortfall (MES) measure of Acharya et al.

(2010), the systemic impact index (SII) and the vulnerability index (VI) of Zhou

(2010) to 16 listed banks in China for the 2007–2014 period.6The former two

are widely used to monitor f‌inancial institutions by central bankers and bank

regulators and have a high impact in academia (Benoit et al., 2013). The latter

two are based on a different estimation method (i.e. extreme value theory).

These measures, calculated using daily equity returns, are used to capture each

bank’s contribution to systemic risk.

We f‌ind that the four measures of systemic risk diverge, as they capture differ-

ent aspects of systemic risk in the banking system. However, the rankings of

banks based on these measures are signif‌icantly correlated. Moreover, the

time-series results for the CoVaR and MES measures suggest that systemic risk

in the Chinese banking system decreased after the global f‌inancial crisis but

started rising in 2014. We also compare our f‌indings for Chinese banks with sim-

ilar results for Korean banks, and f‌ind that Chinese banks have higher ΔCoVaR

and lower MES than Korean banks, suggesting that the Chinese banking system

4As Fenech et al. (2014) point out, loan quality of the Chinese banking system is directly linked to

real estate and government-supported infrastructure projects. Koetter and Poghosyan (2010) also

f‌ind that house price f‌luctuations contribute to bank instability. Pasiouras and Kosmidou (2007)

and Athanasoglou et al. (2008) f‌ind that macroeconomic conditions have a signif‌icant effect on

banks’performance.

5See www.thebanker.com/Top-1000-World-Banks/Top-1000-World-Banks-2014-Back-on-track.

6We also consider the SRISK approach of Brownlees and Engle (2012) but we f‌ind that this ap-

proach may not be applicable to Chinese banks because the results are zero for all banks considered

in the 2007–2010 period, which seems counterintuitive. We provide details of the SRISK measure in

an online Appendix.

Q. HUANG ET AL.2

Pacif‌ic Economic Review

, 24: 2 (2019) pp. 348–372

doi:10.1111/1468-0106.12212