The resilience from the Chinese banking system is a subject of scrutiny in the last decade because of its individual characteristics, for example concentration, rapid asset growth, need for shadow banking, opaque interconnectedness of monetary firms, and also the elevated worldwide clout of Chinese banks. The rapid growth of china stock exchange poses possibilities and risks for worldwide investors and influences the performance of emerging markets, as benchmarked by common indices like the MSCI EM.
Within our paper, we shift focus in the largest, condition-owned, and globally important banks, towards the relatively smaller sized banking institutions and look at whether or not they lead considerably towards the systemic risk and interconnectedness from the Chinese banking sector. We examine if three frequently-discussed causes of fragility-elevated housing prices, economic policy uncertainty, and shadow banking-lead towards the magnitude of systemic risk in banks. We discover that systemic risk continues to be persistently rising following the global financial trouble, with smaller sized institutions, within our sample covering about 50% of total banking assets, adding greater than 40% to total systemic risk in 2017-18. Our results reveal that elevated housing prices, policy uncertainty, and shadow banking function as a reason for systemic risk within the lengthy and short runs. Shadow banking also plays a role in the rise in housing prices. Finally, smaller sized banks seem to be more interconnected than their large counterparts and also have greater influence than formerly believed.
We measure systemic risk by estimating SRISK (Brownlees and Engle, 2016) for any representative sample of 17 listed Chinese banks, while using MSCI EM index like a market performance benchmark. We discuss the evolving interconnectedness of banks utilizing a Granger causality network approach (Billio et al. 2012) and infer lengthy- and short-run causality between systemic risk and explanatory factors from a number of vector error correction models. To determine policy uncertainty, we make use of the EPU and Trade Policy Uncertainty Index (Baker et al 2016), which provides coverage for business and financial aspects news printed within the Hong Kong-based South China Morning Publish. The EPU indices, in line with the frequency and coverage of economic and financial aspects articles in news outlets, have lately gain popularity as indicators of public sentiment, given that they represent qualitative instead of quantitative shifts. For shadow banking, we escape from using entrusted loans as proxy (Allen et al. 2019) and rather choose the more inclusive “Bank Shadow” and “Traditional Shadow Banking” measures suggested in Sun (2019). Traditional Shadow Banking captures credit creation by non-bank financial intermediaries through cash transfer, while Bank Shadow captures money creation by banks through accounting treatments that generate liabilities and moves beyond traditional loans. Housing costs are proxied through the Real House 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 from the Big Four and all sorts of other banks
(b) Group contributions (%) to total SRISK from the Big Four and all sorts of other banks
Figure 1(a) shows the steady rise in total systemic risk after 2011. China banking system was resilient towards the direct financial results of the 2008-09 global financial trouble, largely since it was centered on a strongly growing domestic market coupled with little contact with overseas wholesale funding markets. The big boost in SRISK during 2011-12 coincided using the finish from the 2008 economic stimulus bundle, which brought to some boost in credit volume and asset prices, including housing prices. The 2015 and 2016 Chinese stock exchange crashes were also connected using the large SRISK increase between May 2015 and August 2016. The publish-crisis growth of china housing and shadow banking sectors continues to be extensively recorded, for instance, by Lai and Van Order (2019) and Ding et al. (2017).
Economic policy uncertainty and housing prices affect systemic risk within the lengthy and also the short runs, while both shadow banking measures influence systemic risk over time. Particularly, systemic risk doesn’t appear to affect property prices or Bank Shadow, but there’s a 2-way influence for Traditional Shadow Banking and policy uncertainty. Shadow banking affects housing prices, although not the alternative. This shows the way the financing from the Chinese housing industry has trusted liquidity providers apart from banks. When all factors are taken jointly into consideration, we observe similar relationships. The 4 indicators don’t simply increase together (figure 2), but each one of the three factors plays a role in the increase in systemic risk and it is linked with the remainder. The intuition that housing prices, shadow banking, and uncertainty cause systemic risk for banks and feed into one another seems to carry for China.
Figure 2. two line charts showing figures (a) and b).
Figure 1(b) blogs about the systemic risk contribution from the four greatest banks (Bank of China, Farming Bank of China, Industrial and Commercial Bank of China, and China Construction Bank) with this of other banks. From your 80%-20% split this year in support of the large 4, the main difference has receded to 60%-40% in 2018, which highlights the elevated need for smaller sized banks. The financial institution that influences probably the most other firms is China Retailers Bank, which affects 13 other firms, adopted by China CITIC Bank (12), China Everbright Bank (11), and Farming Bank of China (11). China Everbright Bank (16), Bank of Nanjing (16), Ping An Bank (15), and Chongqing Bank (14) possess the greatest quantity of total connections. This shows that smaller sized firms haven’t only be important individually, but additionally can exert significant sectoral influence. Although we don’t neglect the convenience of intervention of Chinese regulators, our results point toward an growing vulnerability of China’s banking system because of contagion effects.
An worldwide investor should stress about the influence of smaller sized Chinese banks on financial stability and place their systemic and individual riskiness into consideration. Their elevated exposure and connectedness may trigger a systemic event with wider repercussions, considerably bigger than their individual size. Although condition possession prevents a personal bankruptcy much like those of Lehman Siblings, our findings claim that the total cost of distress might be greater than generally assumed. Our primary policy suggestion is the fact that further regulatory changes have to focus not only around the systemically important largest institutions but additionally on smaller sized banks.
Allen, F., Qian, Y., Tu, G., Yu, F., 2019. Entrusted loans: A detailed take a look at China’s shadow banking system. Journal of monetary Financial aspects 133 (1), 18-41.
Baker, S. R., Blossom, N., Davis, S. J., 2016. Calculating economic policy uncertainty. Quarterly Journal of Financial aspects 131 (4), 1593-1636.
Billio, M., Getmansky, M., Lo, A. W., Pelizzon, L., 2012. Econometric measures of connectedness and systemic risk within the finance and insurance sectors. Journal of monetary Financial aspects 104 (3), 535-559.
Brownlees, C., Engle, R. F., 2016. SRISK: A conditional capital shortfall way of measuring systemic risk. Overview of Financial Studies 30 (1), 48-79.
Ding, D., Huang, X., Jin, T., Lam, W. R., 2017. Assessing China’s residential housing market. IMF Working Paper, 17/248.
Lai, R. N., Van Order, R. A., 2019. Shadow banking and also the property market in China. Worldwide Property Review, forthcoming, offered at http://dx.doi.org/10.2139/ssrn.2788012.
Sun, G., 2019. China’s shadow banking: Bank’s shadow and traditional shadow banking. BIS Working Papers, No 822.