Conference on Macro-Financial Linkages and Current Account - PowerPoint PPT Presentation

Conference on Macro-Financial Linkages and Current Account Imbalances Asynchronous Monetary Policies and International Dollar Credit Dong He * , Eric Wong # , Andrew Tsang # , Kelvin Ho # * International Monetary Fund and # Hong Kong Monetary Authority The views and analysis expressed in this presentation are those of the authors, and do not necessarily reflect those of the International Monetary Fund or the Hong Kong Monetary Authority

Background US dollar as the premier currency and the key Questions role of European and Japanese banks in • The US’s monetary normalization may disrupt channelling dollar credit the international US dollar credit US dollar international claims % of all international claims USD bn • Various studies point out that the supply of 18,000 60 global dollar credit is largely influenced by 15,000 50 non-US international banks. (McCauley el al. 2014; Ivashina et al. 2015) 12,000 40 9,000 30 • There is a counter argument that aggressive 6,000 20 monetary policies by the BOJ and the ECB may help cushion the dollar liquidity 3,000 10 0 0 • What would be the net impact on the supply 2000 2002 2004 2006 2008 2010 2012 2014 Banks from other countries US banks of dollar credit? How crucial are the Japanese banks European banks functioning of the FX swap market and banks’ US dollar claims (rhs) default risk? Notes: 1. The claims are vis-à-vis all sectors and include interoffice claims of banks 2. US-dollar international claims include US dollar cross border claims and local credit extended in US dollars in countries other than the US. 3. European banks include those in BE, FR, DE, IT, NE, ES, SE, CH and GB. 2 Source: BIS locational banking statistics (by nationality).

This study • This study attempts to shed light on these issues both theoretically and empirically Theoretical framework • Our theoretical framework is modified from Ivashina et al. (2015) • The framework captures the linkages between: – international banks’ supply of international dollar credit; – central banks’ unconventional monetary policies (UMPs), – functioning of the FX swap market and banks’ default risk • A testable empirical equation can be derived from the model prediction Empirical analysis • Follow recent studies by Ceterolli and Goldberg (2011) and Aiyar et al. (2015) to apply the fixed-effects approach to identify the impact on credit supply (Khwaja and Mian, 2008) • Conduct the empirical analysis on two unique confidential datasets from the BIS and HKMA 3

Contribution • Theoretical: our model highlights that UMPs both in the US and in the home country have an expansionary effect on the supply of international dollar credit • Empirical: this study finds that the expansionary effect of UMPs in Europe and Japan would only partially offset the contractionary effect of the US’s monetary normalization on global liquidity • The net impact is crucially dependent on whether the Fed’s exit would trigger financial market disruption, particularly in the FX swap market • Stress-testing analysis shows there remains a small risk of a notable decline in the supply of international dollar credit through indirect effects of the US monetary normalization on the FX swap market • Characteristics of global banks and the business models of their overseas branches are found to be important factors in affecting the extent of international transmission of UMPs 4

A theoretical framework Funding flows Euro-area bank Loan flows US$ funding € funding F F* € loans( L ) D* D US$ loans( L* ) = US$ funding + FX swaps (S) EU US Bank’s default risk ( P ) FX swap market Asia Swap cost ( w ) 5

Global bank’s profit maximization problem 𝑁𝑏𝑦 𝑀 ∗ , 𝑀, 𝐺 ∗ , 𝐺, 𝑇 : ℎ 𝑀 − 𝑑 𝐺 + 𝑕 𝑀 ∗ − 𝑚 𝐺 ∗ − 𝑞𝐺 ∗ − 𝑥𝑇 (1) subject to two constraints: 𝑀 ∗ = 𝐸 ∗ + 𝐺 ∗ + 𝑇 (2) 𝑀 = 𝐸 + 𝐺 − 𝑇 (3) Given specific functional forms for ℎ 𝑀 , 𝑑 𝐺 , 𝑕 𝑀 ∗ and 𝑚 𝐺 ∗ : 2 , 𝑕 𝑀 ∗ = 𝜄 ∗ 𝑀 ∗ − 𝛾 ∗ L∗ 2 ℎ 𝑀 = 𝜄𝑀 − 𝛾 L 2 2 𝛽 ∗ F∗ 2 𝛽 F 2 2 , 𝑚 𝐺 ∗ = where 𝜄 , 𝜄 ∗ , 𝛾 , 𝛾 ∗ , 𝛽 , 𝛽 ∗ >0 𝑑 𝐺 = 2 The equilibrium dollar loan can be solved and expressed as: 𝑀 ∗ = 1 Ω 𝐸 ∗ − 1 1 𝛽+𝛾 1 1 𝛽+𝛾 1 𝛽 ∗ 𝜄 ∗ Ω 𝐸 + Ω𝛽 ∗ 𝑞 − Ω𝛽𝛾 𝑥 − Ω𝛾 𝜄 + 𝛽𝛾 + (4) Ω where 𝛽 ∗ +𝛾 ∗ 𝛾 ∗ 𝛽+𝛾 Ω = + 𝛾 > 0 𝛽 ∗ 𝛾 ∗ 𝛽𝛾 6

Model prediction Given eq.(4), L* can be represented by: 𝑀 ∗ = 𝛾 1 𝐸 + 𝛾 2 𝐸 ∗ + 𝛾 3 𝑞 + 𝛾 4 𝑥 + 𝛾 5 𝜄 + 𝛾 6 𝜄 ∗ (5) 𝑥ℎ𝑓𝑠𝑓 𝛾 1 , 𝛾 2 and 𝛾 6 > 0; 𝛾 3 , 𝛾 4 and 𝛾 5 < 0 Factors determining USD loans (L*) Model predictions More abundant liquidity in home country (↑ D ) ↑ L * More abundant liquidity in the US (↑ D * ) ↑ L * Higher default risk of bank (↑ p ) ↓ L * Rises in the swap cost (↑ w ) ↓ L * Increase in the demand for home-currency loans (↑ 𝜄 ) ↓ L * Increase in the demand for US-dollar loans (↑ 𝜄 ∗ ) ↑ L * Eq.(5) yields a testable empirical equation which will be carried out using two unique datasets from the BIS and the HKMA. 7

Data for empirical analysis The BIS dataset The HKMA dataset ( BIS locational statistics by nationality of bank ) ( the return of external positions and the return of Assets and liabilities ) Aggregate-level data by nationality of banks Granular bank-level data (foreign bank branches in Hong Kong) By 12 core global bank nationalities Including 37 foreign bank branches (BE, CA, FR, DE, IT, JP, NE, ES, SE, CH, GB and (accounting for about 60% of the total assets of US) foreign bank branches in Hong Kong) Quarterly data of the Hong Kong office’s US-dollar Quarterly data of US-dollar denominated cross- border claims to non-bank sectors by the global denominated external loans to non-bank sectors vis- à-vis counterparty countries banks vis-à-vis 76 counterparty countries 2012Q2 – 2014Q1 2007Q1 – 2014Q2 Both datasets can be structured with home-destination country pairs, which are conducive to a clear identification of the supply-side effect using the econometric approach by Khwaja and Mian (2008). 8

Description of variables Variable Proxy Description for (Using Japanese banks as an example) ∗ ∆𝑀 𝑗𝑘𝑢 Quarterly growth rate of US-dollar claims on non-banks by Japanese banks ∆𝐺𝐹𝐸 𝑢 (Quarterly growth of the Fed’s balance sheet) x (Japanese banks’ reliance on dollar ∗ ∆𝐸 𝑘𝑢 ∗ 𝑉𝑇𝐺 funding from the US market) 𝑘 The ratio of total funding raised by US branch of Japanese banks to total external claims 𝑉𝑇F 𝑘 by Japan in 2012Q2 Quarterly growth rate of the BOJ’s balance sheet ∆𝐼𝐷𝐶 ∆𝐸 𝑘𝑢 𝑘𝑢 ∆𝐷𝐸𝑇 ∆𝑞 𝑘𝑢 The change in the average CDS spread for Japanese banks 𝑘𝑢 ∆𝐷𝐽𝑄 ∆w 𝑘𝑢−1 The change in the FX swap-implied USD interest rate from Yen minus USD LIBOR 𝑘𝑢−1 𝑔 Forecast of nominal GDP growth rate for Japan to control for changes in the demand for ∆𝐻𝐸𝑄 𝜄 𝑘,𝑢 yen loans Destination country-time fixed effect to account for changes in the demand for US- 𝜄 ∗ μ 𝑗𝑢 dollar loans 9

Estimation result using the BIS dataset Variable ∆ HCB jt 0.67 *** ∆ FED t *USF j 5.05 *** ∆ CDS jt -8.12 * ∆ CIP jt-1 -24.92 ** ∆ GDP jt -3.73 *** Country-time fixed effects for destination country i Yes R-squared 0.12 RMSE 0.63 No. of observations 4,577 Notes: 1. j = home country j , i = destination country i 2. Figures in parentheses are t-statistics. 3. Standard errors are clustered by home country and destination country. 4. ***, **, and * respectively indicate significance at the 1%, 5%, and 10% level 10

Scenario analysis: Assumption on central banks’ balance sheets Fed’s balance sheet projection USD trillion Fed balance sheet (lhs) Projection 5.0 100% Quarterly growth rate (rhs) 4.5 80% 4.0 3.5 60% 3.0 2.5 40% 2.0 20% 1.5 1.0 0% 0.5 0.0 -20% Mar-05 Mar-06 Mar-07 Mar-08 Mar-09 Mar-10 Mar-11 Mar-12 Mar-13 Mar-14 Mar-15 Sources: Board of Governors of the Federal Reserve System, IMF international Financial Statistics and author estimates 11

Scenario analysis: Assumption on central banks’ balance sheets BOJ’s balance sheet projection Eurosystem’s balance sheet projection Eurosystem balance sheet (lhs) USD trillion USD trillion BoJ balance sheet in US dollars (lhs) Quarterly growth rate (rhs) 4.0 30% 8.0 30% Quarterly growth rate (rhs) Projection Projection 25% 3.5 7.0 20% 20% 3.0 6.0 15% 10% 2.5 5.0 10% 2.0 0% 4.0 5% 0% 1.5 3.0 -10% -5% 1.0 2.0 -10% -20% 0.5 1.0 -15% 0.0 -30% 0.0 -20% Mar-05 Mar-06 Mar-07 Mar-08 Mar-09 Mar-10 Mar-11 Mar-12 Mar-13 Mar-14 Mar-15 Mar-05 Mar-06 Mar-07 Mar-08 Mar-09 Mar-10 Mar-11 Mar-12 Mar-13 Mar-14 Mar-15 Sources: Bank of Japan and author estimates. Sources: The European Central Bank and author estimates. 12