Systemic risk in the Chinese banking sector


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The resilience of the Chinese banking system has been a subject of scrutiny over the past decade due to its individual characteristics, such as concentration, rapid asset growth, importance of shadow banking, opaque interconnectedness of financial firms, and the increased international clout of Chinese banks. The rapid expansion of the Chinese stock market poses opportunities and risks for international investors and greatly affects the performance of emerging markets, as benchmarked by common indices such as the MSCI EM.

In our paper, we shift focus from the largest, state-owned, and globally important banks, to the relatively smaller financial institutions and examine whether they contribute significantly to the systemic risk and interconnectedness of the Chinese banking sector. We also examine if three often-discussed sources of fragility—increased housing prices, economic policy uncertainty, and shadow banking—contribute to the magnitude of systemic risk in banks. We find that systemic risk has been persistently rising after the global financial crisis, with smaller institutions, in our sample covering about 50% of total banking assets, contributing more than 40% to total systemic risk in 2017–18. Our results show that increased housing prices, policy uncertainty, and shadow banking serve as a cause of systemic risk in the long and short runs. Shadow banking also contributes to the increase in housing prices. Finally, smaller banks appear to be more interconnected than their large counterparts and have greater influence than previously believed.

We measure systemic risk by estimating SRISK (Brownlees and Engle, 2016) for a representative sample of 17 listed Chinese banks, using the MSCI EM index as a market performance benchmark. We discuss the evolving interconnectedness of banks using a Granger causality network approach (Billio et al. 2012) and infer long- and short-run causality between systemic risk and explanatory factors from a series of vector error correction models. To measure policy uncertainty, we use the EPU and Trade Policy Uncertainty Index (Baker et al 2016), which covers business and economics news published in the Hong Kong–based South China Morning Post. The EPU indices, based on the frequency and coverage of business and economics articles in news outlets, have recently become popular as indicators of public sentiment, since they represent qualitative rather than quantitative shifts. For shadow banking, we move away from using entrusted loans as proxy (Allen et al. 2019) and instead select the more inclusive “Bank Shadow” and “Traditional Shadow Banking” measures proposed in Sun (2019). Traditional Shadow Banking captures credit creation by non-bank financial intermediaries through money transfer, while Bank Shadow captures money creation by banks through accounting treatments that generate liabilities and moves beyond traditional loans. Housing prices are proxied by the Real Residential Property Prices index for China from FRED.

A line chart showing Figure (a) Aggregate SRISK 2006-2018.

(a) Aggregate SRISK 2006 – 2018

Line chart showing Figure 1 (b) Group contributions (%) to total SRISK of the Big Four and all other banks

(b) Group contributions (%) to total SRISK of the Big Four and all other banks


Figure 1(a) shows the steady increase in total systemic risk after 2011. The Chinese banking system was resilient to the direct financial effects of the 2008–09 global financial crisis, largely because it was focused on a strongly growing domestic market and had little exposure to overseas wholesale funding markets. The large surge in SRISK during 2011–12 coincided with the end of the 2008 economic stimulus package, which led to a surge in credit volume and asset prices, including housing prices. The 2015 and 2016 Chinese stock market crashes were also associated with the large SRISK increase between May 2015 and August 2016. The post-crisis expansion of the Chinese housing and shadow banking sectors has been well documented, for example, by Lai and Van Order (2019) and Ding et al. (2017).

Economic policy uncertainty and housing prices affect systemic risk in the long and the short runs, while both shadow banking measures influence systemic risk in the long run. Notably, systemic risk does not seem to affect property prices or Bank Shadow, but there is a two-way influence for Traditional Shadow Banking and policy uncertainty. Shadow banking affects housing prices, but not the opposite. This shows how the financing of the Chinese housing market has relied on liquidity providers other than banks. When all factors are taken jointly into account, we observe similar relationships. The four indicators do not simply increase together (figure 2), but each of the three factors contributes to the rise in systemic risk and is linked with the rest. The intuition that housing prices, shadow banking, and uncertainty cause systemic risk for banks and feed into each other appears to hold for China.

Figure 2. two line charts showing figures (a) and b).


Figure 1(b) compares the systemic risk contribution of the four biggest banks (Bank of China, Agricultural Bank of China, Industrial and Commercial Bank of China, and China Construction Bank) with that of all other banks. From an 80%–20% split in 2011 in favor of the Big 4, the difference has receded to 60%–40% in 2018, which highlights the increased importance of smaller banks. The bank that influences the most other firms is China Merchants Bank, which affects 13 other firms, followed by China CITIC Bank (12), China Everbright Bank (11), and Agricultural Bank of China (11). China Everbright Bank (16), Bank of Nanjing (16), Ping An Bank (15), and Chongqing Bank (14) have the highest number of total connections.  This demonstrates that smaller firms have not only become more important individually, but also are able to exert significant sectoral influence. Although we do not disregard the capacity for intervention of Chinese regulators, our results point toward an increasing vulnerability of China’s banking system due to contagion effects.

An international investor should be concerned about the influence of smaller Chinese banks on financial stability and take their systemic and individual riskiness into account.  Their increased exposure and connectedness may trigger a systemic event with wider repercussions, significantly larger than their individual size. Although state ownership may prevent a bankruptcy similar to that of Lehman Brothers, our findings suggest that the overall cost of distress may be higher than commonly assumed. Our main policy suggestion is that further regulatory changes need to focus not just on the systemically important largest institutions but also on smaller banks.


Allen, F., Qian, Y., Tu, G., Yu, F., 2019. Entrusted loans: A close look at China's shadow banking system. Journal of Financial Economics 133 (1), 18–41.

Baker, S. R., Bloom, N., Davis, S. J., 2016. Measuring economic policy uncertainty. Quarterly Journal of Economics 131 (4), 1593–1636.

Billio, M., Getmansky, M., Lo, A. W., Pelizzon, L., 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104 (3), 535–559.

Brownlees, C., Engle, R. F., 2016. SRISK: A conditional capital shortfall measure of systemic risk. Review of Financial Studies 30 (1), 48–79.

Ding, D., Huang, X., Jin, T., Lam, W. R., 2017. Assessing China's residential real estate market. IMF Working Paper, 17/248.

Lai, R. N., Van Order, R. A., 2019. Shadow banking and the property market in China. International Real Estate Review, forthcoming, available at

Sun, G., 2019. China's shadow banking: Bank's shadow and traditional shadow banking. BIS Working Papers, No 822.

Edward HC Graydon
September 28, 2021

While China’s role in global trade is highly publicized and politically polarizing, its growing influence in international finance has remained more obscure, mostly due to a lack of data and transparency. Over the past two decades, China has become a major global lender, with outstanding claims now exceeding more than 5% of global GDP. Almost all of this lending is official, coming from the government and state-controlled entities.

Our research, { Graydon Investments Group LTD} based on a comprehensive new data set, shows that China has extended many more loans to developing countries than previously known. This systematic underreporting of Chinese loans has created a “hidden debt” problem – meaning that debtor countries and international institutions alike have an incomplete picture on how much countries around the world owe to China and under which conditions.

In total, the Chinese state and its subsidiaries have lent about $1.5 trillion in direct loans and trade credits to more than 150 countries around the globe. This has turned China into the world’s largest official creditor — surpassing traditional, official lenders such as the World Bank, the IMF, or all OECD creditor governments combined.

Despite the large size of China’s overseas lending boom, no official data exists on the resulting debt flows and stocks. China does not report on its international lending, and Chinese loans literally fall through the cracks of traditional data-gathering institutions. For example, credit rating agencies, such as Moody’s or Standard & Poor’s, or data providers, such as Bloomberg, focus on private creditors, but China’s lending is state sponsored, and therefore off their radar screen. Debtor countries themselves often do not collect data on debt owed by state-owned companies, which are the main recipients of Chinese loans. In addition, China is not a member of the Paris Club (an informal group of creditor nations) or the OECD, both of which collect data on lending by official creditors

What We Learned About China’s Overseas Lending

Our data show that almost all of China’s lending is undertaken by the government and various state-owned entities, such as public enterprises and public banks. China’s overseas lending boom is unique in comparison to capital outflows from the United States or Europe, which are largely privately driven. We also show that China tends to lend at market terms, meaning at interest rates that are close to those in private capital markets. Other official entities, such as the World Bank, typically lend at concessional, below-market interest rates, and longer maturities. In addition, many Chinese loans are backed by collateral, meaning that debt repayments are secured by revenues, such as those coming from commodity exports.
The People’s Republic has always been an active international lender. In the 1950s and 1960s, when it lent money to other Communist states, China accounted for a small share of world GDP, so the lending had little or no impact on the pattern of global capital flows. Today, Chinese lending is substantial across the globe. The last comparable surge in state-driven capital outflows was the U.S. lending to war-ravaged Europe in the aftermath of World War II, including programs such as the Marshall Plan. But even then, about 90% of the $100 billion (in today’s dollars) spent in Europe comprised grants and aid. Very little came at market terms and with strings attached such as collateral.

On the borrower side, debt is accumulating fast: For the 50 main developing country recipients, we estimate that the average stock of debt owed to China has increased from less than 1% of debtor country GDP in 2005 to more than 15% in 2017. A dozen of these countries owe debt of at least 20% of their nominal GDP to China (Djibouti, Tonga, Maldives, the Republic of the Congo, Kyrgyzstan, Cambodia, Niger, Laos, Zambia, Samoa, Vanuatu, and Mongolia).

Maybe more importantly, our analysis reveals that 50% of China’s loans to developing countries go unreported, meaning that these debt stocks do not appear in the “gold standard” data sources provided by the World Bank, the IMF, or credit-rating agencies. The unreported lending from China has grown to more than $200 billion USD as of 2016.