Slow-burn contagion Eli Remolona Professor of Finance Research - PowerPoint PPT Presentation

Slow-burn contagion Eli Remolona Professor of Finance Research Seminar Series Asia School of Business, 29 July 2020

2 Slow-burn contagion Ø Two kinds of contagion Ø The risk of sudden-stop contagion Ø The risk of slow-burn contagion ü Major lenders of ASEAN+3 ü Measuring network centrality ü The difference the global banking network makes Ø Takeaways and policy

T wo kinds of contagion

4 Current-account balances and bank lending in East Asia 1994-1998 Asian crisis 5 Current accounts and bank lending Billions of US dollars 100 80 Sudden stop 60 40 20 0 -20 -40 -60 1994 1995 1996 1997 1998 -80 Current account Bank lending Source: Radelet and Sachs (1998)

5 Five sudden stops all at the same time Indonesia S Korea Malaysia Philippines Thailand 0.0% -10.0% -20.0% -30.0% -40.0% -50.0% -60.0% -70.0% -80.0% Currency Stocks GDP growth -90.0%

6 What made the deficits unsustainable? Th The f famous se see-th through bu buildi ldings of Ea East Asia

Major lenders on the eve of the crisis

Slow-burn contagion even without sudden stops Source: Koch and Remolona (2018)

The risk of sudden-stop contagion

10 Most current accounts are in surplus Current account as ratio to GDP (Average of 2017-2019) 8.0 6.0 4.0 2.0 0.0 -2.0 China HK Indones Japan Korea Malay Phil Thailnd Vietnam -4.0

11 More importantly, there are few signs of excessive borrowing Credit-to-GDP gaps 30 25.4 2015 2017 2019 25 20 15.5 15 12.9 11.8 11.4 9.5 10 7 6.9 6.5 6.3 4.9 4.4 5 3.8 3.5 0.3 0.3 0 -2.2 -2.9 -5 China Indonesia Japan S Korea Malaysia Thailand

The risk of slow-burn contagion

13 Japanese banks now dominate direct lending to ASEAN ex Singapore Direct lending to ASEAN ex Singapore (USD357 billion in claims as of 2019 Q4) 26% 34% 12% 5% 9% 7% 7% Japan Outside area United Kingdom Chinese Taipei United States Euro area Other

14 UK and US banks lead direct lending to China and South Korea Direct lending to China and South Korea (USD669 billion in claims as of 2019 Q4) 18% 30% 15% 5% 9% 13% 10% United Kingdom Outside area United States Japan Euro area Switzerland Other

But what about the links 15 among global banks? Proportion of lending to total claims on bank counterparty Borrowing banks United United Lending banks Japan Euro Area Kingdom States Japan 8.7% 23.1% 7.6% United Kingdom 21.6% 10.4% 19.2% United States 36.5% 7.1% 8.0% Euro Area 23.0% 58.2% 23.5%

Who exactly are these banks? G-SIBs as of Nov 2019 G-SIBs as of Nov 2019 Bucket 4 Agricultural Bank of China JP Morgan Chase (2.5%) Bank of New York Mellon China Construction Bank Citigroup Bucket 3 Credit Suisse (2.0%) HSBC Groupe BPCE Groupe Crédit Agricole ING Bank Bank of America Mizuho FG Bank of China Morgan Stanley Bucket 1 Barclays (1.0%) Royal Bank of Canada BNP Paribas Santander Bucket 2 Deutsche Bank Société Générale (1.5%) Goldman Sachs Standard Chartered Industrial and Commercial State Street Bank of China Sumitomo Mitsui FG Mitsubishi UFJ FG T oronto Dominion Wells Fargo UBS UniCredit

Measuring network centrality

18 Determining G-SIBs Denominators Indicators Size Basel III total exposure • • Cross-jurisdictional claims Cross-jurisdictional activity Cross jurisdictional liabilities • Intra-financial system claims • Interconnectedness Intra-financial system liabilities • Securities outstanding • • Assets under custody Substitutability/infrastructure • Payments • Underwritten transactions OTC derivatives • Complexity Level 3 assets • Securities trading •

19 Shapley values Ø In a cooperative game, a coalition of players generates a payoff shared by the coalition as a whole Ø The Shapley value divides up that payoff to allocate it among individual players based on their marginal contributions. Ø Tarashev, Tsatsaronis and Borio (2016) apply the Shapley value to measure the systemic risk of large banks, but they don’t take account of borrowers.

20 The cool thing about Shapley values Additivity Symmetry Unique “Dummy axiom” solution Linearity

21 Calculating the characteristic function with two banking systems Japan 26% banks ASEAN 5.7% ex Singapore 9% 22% 0.8% U.K. banks 9%

The difference the global banking network makes

23 How should we worry about global bank contagion? ASEAN ex Singapore 40.0% 34.0% 35.0% 30.0% 26.1% 25.0% 20.0% 14.6% 15.0% 12.8% 9.2% 9.0% 10.0% 7.4% 7.4% 5.0% 0.0% Japan UK Taiwan US Direct Exposure Shapley value

24 China and South Korea might worry about UK and US banks China and South Korea 30.0% 27.6% 25.0% 21.2% 20.0% 17.7% 18.3% 17.4% 15.0% 13.3% 9.6% 10.0% 8.3% 5.0% 0.0% UK US Japan EA Direct exposure Shapley values

25 Takeaways Ø Sudden-stop contagion not so pressing concern Ø But slow-burn contagion a concern, because of a common reliance on a few global banks Ø In the global banking network, the strongest links are between Japanese and US banks and between UK and euro area banks Ø Shapley values suggest hidden risks of slow-burn contagion through US and euro area banks

26 Policy Shapley values in G-SIBs? • Would FSB consider a Shapley-value indicator in setting G-SIB capital buffers? Shapley values in regional SIBs? • Can ASEAN agree on framework for regional SIBs that account for Shapley values? Shapley values in macroprudential measures? • Impose macroprudential tax on foreign borrowing as concentration rises

27 References Ø Allen, F and D Gale (2000): Financial contagion. Journal of Political Economy 108, 1-33. Ø Alves, I et al (2013): The structure and resilience of the European interbank market. ESRB Occasional Paper Series No 3. Ø Basel Committee (2013): Global systemically important banks: updated assessment methodology and the higher loss absorbency requirement (July). Ø Cohen B and E Remolona (2008): Information flows during the Asian crisis: Evidence from closed-end funds. Journal of International Money and Finance . Ø Koch, C and E Remolona (2018): Common lenders in emerging Asia: Their changing roles in three crises. BIS Quarterly Review (March). Ø Mas-Colell A, A Whinston and J Green (1995): Microeconomic Theory . Oxford University Press.

28 References (continued) Ø Moreno, R, G Pasadilla and E Remolona (1998): Asia’s financial crisis: Lessons and policy responses. Pacific Basin Working Paper Series 90-02, Federal Reserve Bank of San Francisco. Ø Moreno, R (2008): Experiences with current-account deficits in Southeast Asia. Current Account and External Financing , ed by K Cowan, S Edwards, and R Valdés, Santiago, Chile. Ø Park, CY and K Shin (2020); Contagion through national and regional exposures to foreign banks during the Global Financial Crisis. Journal of Financial Stability. 46. Ø Sachs, J and S Radelet (1998): The onset of the East Asian financial crisis. NBER Working Paper 6680 (August). Ø Shapley L S (1953): A Value for n-person Games. In Contributions to the Theory of Games , vol II, by H Kuhn and A Tucker, eds. Annals of Mathematical Studies v 28, pp 307– 317. Princeton University Press.

29 References (continued) Ø Tarashev, N, K Tsatsaronis and C Borio (2016): Risk attribution using the Shapley value: Methodology and policy applications. Review of Finance 20, 1189-1213.

Current accounts are driven 30 by common factors Explaining changes in the ratios of current accounts to GDP of ASEAN, China and Korea, 2010:Q4-2018:Q4 32% 33% 19% 16% 1st PC 2nd PC 3rd PC Other

Some current accounts do tend 31 to move together Loadings on the 1st factor 2010 Q1-2018 Q4 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 IND MAL THA VIE PHI KOR CHI -1