## 4. Subsequent Cumulative Change in Domestic Financial Conditions: A 100-basis point Shock to Cross-Currency Basis, High Stress vs. Low Stress, by Net IIP Positions

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### Introduction and motivation
- Covered interest parity (CIP) is a non-arbitrage condition; since the global financial crisis (GFC) CIP has failed to hold for numerous currencies vis-à-vis the U.S. dollar.
- A negative cross-currency basis implies it costs more to obtain U.S. dollars via the FX swap market than by borrowing directly in the U.S. dollar cash market.
- Key regional concerns:
  - Asian countries heavily rely on the U.S. dollar for financial intermediation and trade.
  - FX hedging demand in Asia (in U.S. dollar terms) has increased rapidly—by some estimates almost twice as fast as GDP growth in Asia.
  - Demographics (rapid aging) and divergence of monetary policy stances can sustain or raise FX hedging demand and pressures in FX derivatives markets.

### Documented developments of the cross-currency basis in Asia
- CIP deviations opened up during the GFC and, despite narrowing after the crisis peaks, persistent gaps remain.
- Representative magnitudes and observations:
  - The average basis at the peak of the GFC in 2008Q4 for countries with negative basis was about minus 70 basis points.
  - New Zealand and Canada showed positive basis averaged around 10 basis point at the GFC peak.
  - At the end of 2018, the average cross-currency basis for major currencies and Asian countries in Figure 2 was around negative 30 basis points.
  - 2016Q4 was an exception with large and negative basis (e.g., Japanese yen-U.S. dollar cross-currency basis dropped to negative 70 basis point).
- Cross-country and time-series variation is significant: some countries (Australia, New Zealand) have positive basis; others (Japan, Korea) show negative basis.

### Factors driving CIP deviations
- Counterparty and credit risks during crisis periods
  - Counterparty credit and funding liquidity risk spiked during the GFC, reflected in a sharp increase of the LIBOR-OIS spread.
  - U.S. dollar shortage in the interbank market led participants to the FX derivatives market, spilling interbank stress into FX swaps and widening CIP deviations.
- Regulatory reforms
  - Post-crisis tighter regulation (leverage ratio, risk-weighted capital requirements) increased the cost of expanding balance sheets to arbitrage CIP gaps.
  - Basel leverage ratio of 3 percent raised required returns on CIP arbitrage by making short-term CIP trades balance-sheet intensive.
  - Nonbank arbitrageurs (hedge funds, MMFs, reserve managers) also faced funding constraints (e.g., October 2016 MMF reforms).
- FX hedging demand and structural imbalances
  - FX hedging demand proxied by net international investment position (IIP) and bank net foreign assets correlates with cross-currency basis:
    - Countries with positive net IIP tend to have more negative cross-currency basis (higher premium to borrow and hedge U.S. dollars).
    - Classification:
      - Positive basis: Australia, India, New Zealand — generally negative net IIP, net debtor countries.
      - Near-zero basis: Philippines, Singapore, Thailand — balanced external funding positions.
      - Negative basis: Japan, Korea, (Hong Kong SAR to a lesser degree) — net creditor nations with positive net IIPs.
  - Country-specific highlights:
    - Japan: largest U.S.-dollar denominated claims; banks drive hedging demand; Japan’s net IIP close to US$ 3 billion at the end of 2018.
    - Korea: severe CIP deviations during the crisis; structural demand-supply imbalance in FX forwards; life insurers target high hedging ratios.
    - Australia: banks have large net forex liability position (20 percent of GDP), contributing to persistent positive basis.
  - Institutional investors have become major participants in FX derivatives markets:
    - Institutional investors account for nearly 65 percent of the increase in FX-hedging demand in Asia since 2007 (Goldman Sachs (2018)).
    - In Korea, life insurers’ hedging demand is estimated to exceed banks’ hedging demand by five-fold.
    - Korean life insurers’ foreign assets could more than double by 2022, reaching US$ 280 billion.
- U.S. dollar supply factors
  - Foreign reserve managers and sovereign wealth funds can supply U.S. dollar liquidity via cross-currency swaps, but supply appears procyclical (can retract in stress).
- Broad dollar index correlation
  - Movements in the broad dollar index and the basis in Asia have largely mirrored each other since the GFC; a stronger U.S. dollar correlates with wider CIP deviations.

### Implications for domestic financial conditions — conceptual framework
- Effects of higher U.S. dollar funding/hedging costs differ by net position:
  - Net creditor economies (positive net IIP): higher hedging costs can lower the attractiveness of overseas investment; inward shift to onshore assets may improve domestic financial conditions.
  - Net debtor economies (negative net IIP): higher hedging costs can tighten domestic financial conditions as banks/corporates shrink balance sheets and domestic interest rates rise.
- Non-linearities expected during periods of high financial stress (VIX threshold defined below).

### Empirical approach and key empirical inputs
- Method: local projections (Jordà, 2005), monthly frequency from January 2008 to December 2018.
- Sample: 25 economies (advanced and emerging) including Australia, Canada, Denmark, Euro Area, Hong Kong SAR, Korea, Japan, New Zealand, Norway, Sweden, Switzerland, U.K., India, Indonesia, Malaysia, Philippines, Singapore, Thailand, Brazil, Chile, Colombia, Mexico, South Africa, Turkey, and others.
- Baseline shock: a 100-basis point shock to the change in cross-currency basis (monthly percent change of the cross-currency basis rate over one year is used as the shock measure).
- Statistical context for the shock: the mean change in cross-currency basis in the sample is negative 0.05 basis point, with a standard deviation of 9.6.
- High financial stress definition: VIX larger than two standard deviations above the historical mean (from January 2000), which is about 35 (19 + 2*8.06).

### Empirical results — average effects (Hypothesis 1)
- On average, a 100-basis point shock to cross-currency basis leads to tighter domestic financial conditions across all countries:
  - Corporate spread increases by maximum 20 basis point after 6-7 months, cumulatively (all countries).
- Selected reported sequences from Table 1 (Subsequent cumulative change in domestic financial conditions : a 100-basis point shock to cross-currency basis), change in CCBS by horizon (months 1 to 10) with reported robust s.e. in parentheses:
  - Corporate Spread: 0.48 1.04 1.22 1.34 1.76 2.10 2.03 1.98 1.66 1.99 (robust s.e.: (0.34)(0.52)(0.61)(0.66)(0.70)(0.89)(0.87)(0.87)(0.81)(0.78)). R_square: 0.146 0.195 0.206 0.209 0.230 0.213 0.211 0.220 0.252 0.286. Number of observations: 1,469 1,463 1,453 1,442 1,430 1,418 1,406 1,394 1,382 1,370.
  - Sovereign Spread: 0.62 2.74 3.53 4.36 2.44 3.89 2.48 2.18 3.12 2.60 (s.e.: (0.14)(0.18)(0.21)(0.23)(0.24)(0.27)(0.26)(0.26)(0.26)(0.28)). R_square: 0.172 0.171 0.150 0.155 0.149 0.150 0.152 0.167 0.176 0.184. Number of observations: 1,696 1,688 1,676 1,663 1,649 1,635 1,621 1,607 1,593 1,579.
  - Interbank Spread 3M: 3.03 5.47 6.17 3.91 5.82 3.81 4.35 2.28 3.22 3.30 (s.e.: (0.29)(0.36)(0.31)(0.31)(0.32)(0.32)(0.33)(0.31)(0.29)(0.30)). R_square: 0.216 0.203 0.160 0.154 0.145 0.142 0.142 0.133 0.135 0.140. Number of observations: 1,624 1,616 1,603 1,590 1,576 1,562 1,548 1,534 1,520 1,506.

- Effects differ by net IIP position (Table 2 — Subsequent cumulative change by Net IIP positions):
  - Corporate Spread — Net Negative IIP (months 1 to 10): -5.39 -8.86 -7.55 -8.38 -8.82 -7.35 -6.48 -6.80 -15.09 -7.55 (s.e.: (0.25)(0.31)(0.42)(0.45)(0.54)(0.60)(0.64)(0.67)(0.69)(0.68)). Number of observations: 607 603 598 593 588 583 578 573 568 563.
  - Corporate Spread — Net Positive IIP (months 1 to 10): 11.25 17.83 20.27 27.29 27.54 29.97 31.43 35.36 34.43 31.83 (s.e.: (0.57)(0.67)(0.81)(0.92)(0.92)(1.19)(1.17)(1.24)(1.16)(1.09)). Number of observations: 862 860 855 849 842 835 828 821 814 807.
  - Sovereign Spread — Net Negative IIP (months 1 to 10): 3.50 4.87 6.60 8.19 2.88 5.09 2.89 1.65 4.48 3.48 (s.e.: (0.26)(0.31)(0.36)(0.39)(0.39)(0.43)(0.43)(0.45)(0.44)(0.48)). Number of observations: 843 841 836 830 823 816 809 802 795 788.
  - Sovereign Spread — Net Positive IIP (months 1 to 10): -2.46 -1.94 -3.15 -1.64 -2.47 -2.32 -0.81 -0.28 -1.49 -0.53 (s.e.: (0.12)(0.18)(0.14)(0.16)(0.15)(0.17)(0.18)(0.19)(0.19)(0.20)). Number of observations: 853 847 840 823 826 819 812 805 798 791.
  - Interbank Spread 3M — Net Positive IIP (months 1 to 10): 7.48 12.00 12.28 8.03 11.14 8.21 8.67 5.14 6.68 6.76 (s.e.: (0.43)(0.50)(0.48)(0.47)(0.45)(0.44)(0.48)(0.47)(0.46)(0.47)). Number of observations: 819 817 811 805 798 791 784 777 770 763.
  - Interbank Spread 3M — Net Negative IIP (months 1 to 10): -2.03 -3.74 -3.10 -3.21 -2.90 -3.33 -2.07 -1.58 -2.13 -1.10 (s.e.: (0.10)(0.16)(0.15)(0.18)(0.21)(0.21)(0.24)(0.26)(0.27)(0.28)). Number of observations: 805 799 792 785 778 771 764 757 750 743.

- Interpretation:
  - Net creditor (positive net IIP) countries tend to experience declines in spreads (easing domestic financial conditions) following a 100-basis point widening of the cross-currency basis.
  - Net debtor (negative net IIP) countries experience increases in spreads (tightening domestic financial conditions), with corporate spreads responding most sensitively.

### Empirical results — non-linearities during high financial stress (Hypothesis 2)
- High-stress periods (VIX > about 35) amplify the tightening of domestic financial conditions overall:
  - In the pooled sample, a 100-basis point basis shock produces larger increases in corporate, sovereign, and interbank spreads during high-stress periods than during calm times.
- Selected reported sequences from Table 3 (High-stress vs. Low-stress periods), months 1 to 10:
  - Corporate Spread — High Stress: 1.38 3.40 3.98 4.44 5.88 7.87 6.57 5.69 4.59 5.68 (s.e.: (1.17)(1.71)(1.90)(2.02)(2.18)(3.44)(2.89)(2.27)(2.11)(2.17)). Corporate Spread — Low Stress: 0.19 1.17 0.86 0.81 1.07 1.29 1.95 1.98 1.57 1.65 (s.e.: (0.39)(0.49)(0.53)(0.58)(0.67)(0.74)(0.73)(0.79)(0.76)(0.68)). Number of observations: 1469 1463 1453 1442 1430 1418 1406 1394 1382 1370.
  - Sovereign Spread — High Stress: 0.95 1.20 1.20 1.05 1.02 0.54 0.94 0.72 0.70 0.83 (s.e.: (0.24)(0.25)(0.26)(0.30)(0.27)(0.28)(0.34)(0.34)(0.37)(0.35)). Sovereign Spread — Low Stress: 0.20 0.22 0.42 0.50 0.16 0.32 0.18 0.13 0.22 0.18 (s.e.: (0.18)(0.25)(0.28)(0.30)(0.30)(0.34)(0.32)(0.34)(0.32)(0.34)). Number of observations: 1692 1688 1676 1663 1649 1635 1621 1607 1593 1579.
  - Interbank Spread 3M — High Stress: 0.95 1.20 1.20 1.05 1.02 0.54 0.94 0.72 0.70 0.83 (s.e.: (0.43)(0.62)(0.59)(0.59)(0.60)(0.54)(0.46)(0.44)(0.47)(0.41)). Interbank Spread 3M — Low Stress: 0.00 0.18 0.42 0.24 0.37 0.19 0.04 -0.14 0.13 0.02 (s.e.: (0.15)(0.20)(0.31)(0.31)(0.28)(0.33)(0.39)(0.36)(0.28)(0.32)). Number of observations: 1624 1616 1603 1590 1576 1562 1548 1534 1520 1506.

- High-stress effects by net IIP (Table 4 — High-stress vs. Low-stress periods, by Net IIP positions), months 1 to 10:
  - Corporate Spread — Net Positive IIP:
    - High Stress: -0.50 -1.03 -0.83 -0.94 -1.71 -1.58 -1.34 -2.18 -4.18 -4.27 (s.e.: (0.59)(0.80)(0.93)(0.80)(0.96)(0.73)(0.83)(1.14)(0.78)(0.74)). Low Stress: 0.15 -0.08 -0.18 -0.19 -0.24 -0.32 -0.31 -0.57 -0.77 -0.71 (s.e.: (0.30)(0.42)(0.47)(0.42)(0.37)(0.34)(0.42)(0.46)(0.42)(0.46)). Number of observations: 607 603 598 593 588 583 578 573 568 563.
  - Corporate Spread — Net Negative IIP:
    - High Stress: 3.39 6.05 8.74 13.91 10.32 12.25 11.51 11.33 12.35 10.43 (s.e.: (3.33)(3.21)(4.02)(4.13)(3.71)(6.42)(5.11)(3.59)(3.45)(3.76)). Low Stress: 0.01 0.94 0.33 0.13 0.24 0.57 1.24 0.92 0.21 0.09 (s.e.: (0.59)(0.73)(0.74)(0.81)(0.96)(1.12)(1.11)(1.13)(1.12)(1.04)). Number of observations: 807 860 855 849 842 835 828 821 814 (as reported).
  - Sovereign Spread — Net Positive IIP:
    - High Stress: -0.87 -0.94 -0.10 -0.41 -0.60 -0.51 -0.32 -0.69 -0.88 -0.38 (s.e.: (0.32)(0.38)(0.48)(0.42)(0.46)(0.56)(0.65)(0.71)(0.78)(0.76)). Low Stress: -0.21 -0.46 -0.23 -0.46 -0.27 -0.51 -0.22 -0.04 -0.08 -0.29 (s.e.: (0.28)(0.32)(0.37)(0.40)(0.42)(0.41)(0.43)(0.45)(0.47)(0.50)). Number of observations: 487 484 480 476 472 468 464 460 456 452.
  - Sovereign Spread — Net Negative IIP:
    - High Stress: 1.51 0.87 1.46 2.25 2.29 3.25 3.17 2.82 3.78 3.53 (s.e.: (1.19)(0.91)(0.87)(1.13)(1.16)(1.34)(1.45)(1.59)(1.87)(1.90)). Low Stress: 0.34 0.54 0.78 0.87 0.25 0.45 0.15 0.06 -0.11 -0.22 (s.e.: (0.27)(0.40)(0.44)(0.48)(0.48)(0.56)(0.53)(0.60)(0.56)(0.59)). Number of observations: 843 841 836 830 823 816 809 802 795 788.
  - Interbank Spread 3M — Net Positive IIP:
    - High Stress: -0.10 -0.56 -0.35 -0.39 -0.11 -0.20 0.11 0.21 0.24 0.45 (s.e.: (0.31)(0.40)(0.40)(0.43)(0.47)(0.53)(0.58)(0.60)(0.64)(0.68)). Low Stress: -0.11 0.03 -0.10 -0.10 -0.06 -0.06 0.03 0.17 0.16 0.18 (s.e.: (0.12)(0.12)(0.17)(0.16)(0.17)(0.18)(0.19)(0.20)(0.21)(0.23)). Number of observations: 805 799 792 785 778 771 764 757 750 743.
  - Interbank Spread 3M — Net Negative IIP:
    - High Stress: 2.67 3.85 2.73 2.18 3.92 1.27 2.15 1.43 1.49 1.32 (s.e.: (0.53)(0.56)(0.96)(0.83)(0.76)(0.76)(0.64)(0.77)(0.95)(0.88)). Low Stress: 0.15 0.29 0.72 0.66 0.48 0.14 0.14 -0.24 0.14 0.01 (s.e.: (0.25)(0.31)(0.45)(0.44)(0.39)(0.47)(0.55)(0.53)(0.47)(0.52)). Number of observations: 819 817 811 805 798 791 784 777 770 763.

- Interpretation:
  - Net creditor economies: spreads tend to decline (easing) in response to a basis shock, with declines larger during high-stress periods (consistent with flight-to-safety and repatriation of investment).
  - Net debtor economies: experience amplified tightening during high-stress periods; limited domestic alternatives and higher U.S.-dollar funding premia force balance-sheet reductions and fire sales.

### Policy implications and recommendations
- Key takeaways: deviations from CIP are driven by cyclical and structural factors (regulatory changes, demand-supply mismatch for U.S. dollars) and are likely to persist due to demographics and regulation. Heightened risk during market stress can sharply raise hedging costs and trigger severe funding problems for U.S.-dollar-reliant institutions.
- Policy options:
  - Enhanced monitoring
    - Regulators should monitor maturity and currency mismatches in FX hedging via market-based instruments; cross-currency swap rollover risks (3- to 6-month contracts) make institutions vulnerable.
  - Hedging counterparty of last resort
    - Central banks with ample reserves may consider an instrument to act as a “hedging counterparty of last resort” (e.g., long-term FX swap or auction facility) to help unwind open FX positions in extreme distress, with high surcharge to mitigate moral hazard.
  - Investing international reserves in the cross-currency swap market
    - Central banks or sovereign wealth funds could invest a portion of reserves in cross-currency swaps to expand stable U.S. dollar funding supply and help arbitrage away CIP deviations (examples and prior practice are noted).
  - Developing alternative sources of longer-term domestic funding
    - Deepening domestic capital markets (long-term corporate and sovereign bond markets) and developing institutional investors (pension funds, insurance companies) can provide alternatives to U.S. dollar funding and smooth balance-sheet adjustments.
    - Deeper domestic markets also support development of onshore hedging markets (FX deliverable forwards, other derivatives).

### Annex — A Simple Model of FX Hedging Demand (model structure and implications)
- Purpose: present a simple model of a non-U.S. bank’s demand for FX swaps to understand implications of a higher basis and FX hedging costs on domestic financial conditions. Model based on Ivashina et al. (2015) and Iida et al. (2018).
- Agents and assets:
  - Bank invests in USD-denominated assets (loans and bonds, etc.) issued by borrowers in the United States (L_USD), and local currency (LCU)-denominated assets (L_L).
  - Expected gross returns: g_L(·) and g_USD(·), concave functions of amounts of loans.
  - Aggregate lending constraint: L_USD + L_L ≤ K.
  - Default probability: p; size of default: α.
- Funding sources (liabilities): LCU-denominated funding (D_L), USD-denominated funding (D_USD), and FX swaps (S).
- Cost and return functions (as printed in source):
  - g_L(L_L) = (1 + q) L_L − τ_L 2 (L_L)^2
  - g_USD(L_USD) = (1 + q_USD) L_USD − τ_USD 2 (L_USD)^2
  - 표표_L(D_L) = (1+r) D_L + η_L 2 m m m m( D_L )^2
  - 표표_USD(D_USD) = (1+ r_USD + p α) D_USD + η_USD 2 (D_USD)^2
  - FX swap funding cost = (r_USD − r − ∆) S, where ∆ is the cross-currency basis (when CIP holds, ∆ = 0).
  - Minimum liquidity needs: V.
- Bank’s profit maximization problem (as printed in source):
  - max_{L_L, L_USD, D_L, D_USD, S} { g_L(L_L) + g_USD(L_USD) − 표표_L(D_L) − 표표_USD(D_USD) − (r_USD − r − ∆) S } subject to M ≥ V; L_L = D_L − S; L_USD + M = D_USD + S.
- First-order conditions and symmetry assumption (τ_L = τ_USD and η_L = η_USD). Optimal asset holdings (equations reproduced verbatim in the source):
  - Equation (1):
    L_USD =
    1
    τ + η
    {�1+ η 2 τ � (q_USD − r_USD) − η 2 τ (q − r) − τ + η 2 τ ∆ − p 2 α − η 2 V }
  - Equation (2):
    L_L =
    1
    τ + η
    {�1− η 2 τ � (q − r) − η 2 τ (q_USD − r_USD) + τ + η 2 τ ∆ − p 2 α − η 2 V }
- Comparative-static implications (explicitly stated in the source):
  - An increase in cross-currency basis (∆) will lead banks to increase their assets in local currency, and decrease U.S. dollar-denominated assets. (Consistent with Ivashina et al. (2017).)
  - An increase in the U.S. policy rates (q_USD ↑, holding other terms constant) leading to wider interest rate differentials will, ceteris paribus, lead banks to invest more in U.S. dollar-denominated assets and less in local-currency-denominated assets.
- Net liabilities (Net Negative IIP) case implications:
  - If the bank has a net liabilities position vis-à-vis the U.S. dollar and domestic financing cost (η_L) is very high, the bank relies heavily on USD funding (D_USD). An increase in cross-currency basis (∆) reduces FX swap funding (S), and domestic financing cannot be raised to offset the reduction. The reduction in total liabilities forces the bank to reduce overall assets, which tightens domestic financial conditions.
- Joint movements:
  - If both U.S. rates and the cross-currency basis rise simultaneously (as suggested by Avdjiev et al. 2017), the net effect on bank portfolio choices is ambiguous: higher U.S. rates incentivize more USD asset investment, but higher FX hedging costs (∆) lower hedge-adjusted USD returns. Net effect depends on parameter values.

*Source: wpiea2019169-print-pdf - References and selected chapter content reproduced exactly from the source PDF.*

### References .............................................................................................................

### References

### Figures
- 1. Cross-Currency Basis for Selected Economies............................................................. 4
- 2. Cross-Currency Basis for Selected Asian Economies .................................................. 8
- 3. LIBOR-OIS Spread in Selected Currencies ................................................................ 10
- 4. Net Foreign Assets Position and Cross-Currency Basis ............................................. 11
- 5. Correlation between Change in Cross-Currency Basis and Domestic Financial Conditions: Japan vs. Indonesia .................................................................................. 17
- 6. Impulse Response Functions: A 100-basis point S hock to Cross-Currency Basis ..... 18
- 7. Impulse Response Functions: A 100-basis point S hock to Cross-Currency Basis by Net IIP Positions .................................................................................................... 19
- 8. Impulse Response functions: A 100-basis point Shock to Cross-Currency Basis, High Stress vs. Low Stress.......................................................................................... 20
- 9. Impulse Response Functions: A 100-basis point S hock to Cross-Currency Basis, High Stress vs. Low Stress, by Net IIP Position ......................................................... 21
- 10. Balance Sheet of a Non-U.S. Bank with Positive Demand for FX Swaps ................. 32

### Tables
- 1. Subsequent Cumulative Change in Domestic Financial Conditions : A 100-basis point Shock to Cross-Currency Basis ......................................................................... 26
- 2. Subsequent Cumulative Change in Domestic Financial Conditions: A 100-basis point Shock to Cross-Currency Basis by Net IIP Positions ........................................ 27
- 3. Subsequent Cumulative Change in Domestic Financial Conditions: A 100-basis point S hock to Cross-Currency Basis, High Stress vs. Low Stress ..   ....................28

*Source: wpiea2019169-print-pdf - References (page listing of figures and tables).*

### 4. Subsequent Cumulative Change in Domestic Financial Conditions: A 100-basis

### 4. Subsequent Cumulative Change in Domestic Financial Conditions: A 100-basis point Shock to Cross-Currency Basis, High Stress vs. Low Stress, by Net IIP Positions

### Introduction and motivation
- Covered interest parity (CIP) is a non-arbitrage condition; since the global financial crisis (GFC) CIP has failed to hold for numerous currencies vis-à-vis the U.S. dollar.
- A negative cross-currency basis implies it costs more to obtain U.S. dollars via the FX swap market than by borrowing directly in the U.S. dollar cash market.
- Key regional concerns:
  - Asian countries heavily rely on the U.S. dollar for financial intermediation and trade.
  - FX hedging demand in Asia (in U.S. dollar terms) has increased rapidly—by some estimates almost twice as fast as GDP growth in Asia.
  - Demographics (rapid aging) and divergence of monetary policy stances can sustain or raise FX hedging demand and pressures in FX derivatives markets.

### Documented developments of the cross-currency basis in Asia
- CIP deviations opened up during the GFC and, despite narrowing after the crisis peaks, persistent gaps remain.
- Representative magnitudes and observations:
  - The average basis at the peak of the GFC in 2008Q4 for countries with negative basis was about minus 70 basis points.
  - New Zealand and Canada showed positive basis averaged around 10 basis point at the GFC peak.
  - At the end of 2018, the average cross-currency basis for major currencies and Asian countries in Figure 2 was around negative 30 basis points.
  - 2016Q4 was an exception with large and negative basis (e.g., Japanese yen-U.S. dollar cross-currency basis dropped to negative 70 basis point).
- Cross-country and time-series variation is significant: some countries (Australia, New Zealand) have positive basis; others (Japan, Korea) show negative basis.

### Factors driving CIP deviations
- Counterparty and credit risks during crisis periods
  - Counterparty credit and funding liquidity risk spiked during the GFC, reflected in a sharp increase of the LIBOR-OIS spread.
  - U.S. dollar shortage in the interbank market led participants to the FX derivatives market, spilling interbank stress into FX swaps and widening CIP deviations.
- Regulatory reforms
  - Post-crisis tighter regulation (leverage ratio, risk-weighted capital requirements) increased the cost of expanding balance sheets to arbitrage CIP gaps.
  - Basel leverage ratio of 3 percent raised required returns on CIP arbitrage by making short-term CIP trades balance-sheet intensive.
  - Nonbank arbitrageurs (hedge funds, MMFs, reserve managers) also faced funding constraints (e.g., October 2016 MMF reforms).
- FX hedging demand and structural imbalances
  - FX hedging demand proxied by net international investment position (IIP) and bank net foreign assets correlates with cross-currency basis:
    - Countries with positive net IIP tend to have more negative cross-currency basis (higher premium to borrow and hedge U.S. dollars).
    - Classification:
      - Positive basis: Australia, India, New Zealand — generally negative net IIP, net debtor countries.
      - Near-zero basis: Philippines, Singapore, Thailand — balanced external funding positions.
      - Negative basis: Japan, Korea, (Hong Kong SAR to a lesser degree) — net creditor nations with positive net IIPs.
  - Country-specific highlights:
    - Japan: largest U.S.-dollar denominated claims; banks drive hedging demand; Japan’s net IIP close to US$ 3 billion at the end of 2018.
    - Korea: severe CIP deviations during the crisis; structural demand-supply imbalance in FX forwards; life insurers target high hedging ratios.
    - Australia: banks have large net forex liability position (20 percent of GDP), contributing to persistent positive basis.
  - Institutional investors have become major participants in FX derivatives markets:
    - Institutional investors account for nearly 65 percent of the increase in FX-hedging demand in Asia since 2007 (Goldman Sachs (2018)).
    - In Korea, life insurers’ hedging demand is estimated to exceed banks’ hedging demand by five-fold.
    - Korean life insurers’ foreign assets could more than double by 2022, reaching US$ 280 billion.
- U.S. dollar supply factors
  - Foreign reserve managers and sovereign wealth funds can supply U.S. dollar liquidity via cross-currency swaps, but supply appears procyclical (can retract in stress).
- Broad dollar index correlation
  - Movements in the broad dollar index and the basis in Asia have largely mirrored each other since the GFC; a stronger U.S. dollar correlates with wider CIP deviations.

### Implications for domestic financial conditions — conceptual framework
- Effects of higher U.S. dollar funding/hedging costs differ by net position:
  - Net creditor economies (positive net IIP): higher hedging costs can lower the attractiveness of overseas investment; inward shift to onshore assets may improve domestic financial conditions.
  - Net debtor economies (negative net IIP): higher hedging costs can tighten domestic financial conditions as banks/corporates shrink balance sheets and domestic interest rates rise.
- Non-linearities expected during periods of high financial stress (VIX threshold defined below).

### Empirical approach and key empirical inputs
- Method: local projections (Jordà, 2005), monthly frequency from January 2008 to December 2018.
- Sample: 25 economies (advanced and emerging) including Australia, Canada, Denmark, Euro Area, Hong Kong SAR, Korea, Japan, New Zealand, Norway, Sweden, Switzerland, U.K., India, Indonesia, Malaysia, Philippines, Singapore, Thailand, Brazil, Chile, Colombia, Mexico, South Africa, Turkey, and others.
- Baseline shock: a 100-basis point shock to the change in cross-currency basis (monthly percent change of the cross-currency basis rate over one year is used as the shock measure).
- Statistical context for the shock: the mean change in cross-currency basis in the sample is negative 0.05 basis point, with a standard deviation of 9.6.
- High financial stress definition: VIX larger than two standard deviations above the historical mean (from January 2000), which is about 35 (19 + 2*8.06).

### Empirical results — average effects (Hypothesis 1)
- On average, a 100-basis point shock to cross-currency basis leads to tighter domestic financial conditions across all countries:
  - Corporate spread increases by maximum 20 basis point after 6-7 months, cumulatively (all countries).
- Effects differ by net IIP position:
  - Net positive IIP (net creditor countries)
    - A 100-basis point widening of the cross-currency basis leads to about 80 basis point decline in corporate spread for 5 months.
    - Sovereign spread declines by 32 basis points in 3 months.
    - Cumulative decline in interbank spread ranges around 20 to 30 basis points.
    - Interbank spreads tend to decline by 30 to 80 basis points, cumulatively, within 3 months in response to a negative 100-basis point shock (summary magnitude reported).
  - Net negative IIP (net debtor countries)
    - Spreads increase in response to a negative shock to the cross-currency basis.
    - Corporate spreads respond most sensitively by 300 basis points (reported peak response).
    - For net debtor countries, spreads increase by 50 to 100 basis points (reported summary magnitude).
- Interpretation: CIP deviations can mitigate or exacerbate shocks to domestic financial conditions depending on a country’s net IIP and balance-sheet constraints.

### Empirical results — non-linearities during high financial stress (Hypothesis 2)
- High-stress periods (VIX > about 35) amplify the tightening of domestic financial conditions overall:
  - In the pooled sample, a 100-basis point basis shock produces larger increases in corporate, sovereign, and interbank spreads during high-stress periods than during calm times.
- By net IIP:
  - Net creditor economies
    - Contrary to pooled sample, spreads decline rather than increase in response to the same basis shock.
    - Declines are larger during high-stress periods than in low-stress periods (corporate spreads decline within the first month of the shock; effects are statistically significant).
    - Interpretation: investors in net creditor countries shift to domestic assets more during high stress (flight-to-safety and higher hedging costs), easing domestic financial conditions.
  - Net debtor economies
    - Hit harder during high-stress periods: increases in U.S.-dollar funding premium plus limited domestic alternatives force balance-sheet reductions and fire sales, tightening domestic financial conditions more during stress.

### Policy implications and recommendations
- Key takeaways: deviations from CIP are driven by cyclical and structural factors (regulatory changes, demand-supply mismatch for U.S. dollars) and are likely to persist due to demographics and regulation. Heightened risk during market stress can sharply raise hedging costs and trigger severe funding problems for U.S.-dollar-reliant institutions.
- Policy options:
  - Enhanced monitoring
    - Regulators should monitor maturity and currency mismatches in FX hedging via market-based instruments; cross-currency swap rollover risks (3- to 6-month contracts) make institutions vulnerable.
  - Hedging counterparty of last resort
    - Central banks with ample reserves may consider an instrument to act as a “hedging counterparty of last resort” (e.g., long-term FX swap or auction facility) to help unwind open FX positions in extreme distress, with high surcharge to mitigate moral hazard.
  - Investing international reserves in the cross-currency swap market
    - Central banks or sovereign wealth funds could invest a portion of reserves in cross-currency swaps to expand stable U.S. dollar funding supply and help arbitrage away CIP deviations (examples and prior practice are noted).
  - Developing alternative sources of longer-term domestic funding
    - Deepening domestic capital markets (long-term corporate and sovereign bond markets) and developing institutional investors (pension funds, insurance companies) can provide alternatives to U.S. dollar funding and smooth balance-sheet adjustments.
    - Deeper domestic markets also support development of onshore hedging markets (FX deliverable forwards, other derivatives).

*Source: IMF working paper chapter as provided.*

### REFERENCES

### REFERENCES

### Key citations
- Arai, F., Y. Makabe, Y. Okawara and T. Nagano, 2016. Recent Trends in Cross-Currency Basis. Bank of Japan Review 2016-E-7.
- Arregui, N., S. Elekdag, G. Gelos, R. Lafarguette, and D. Seneviratne, 2018. Can Countries Manage Their Financial Conditions Amid Globalization? IMF Working Paper No. 18/15.
- Avdjiev, S., W. Du, C. Koch and H. Shin, 2017. The Dollar, Bank Leverage and the Deviation from Covered Interest Parity. BIS Working Papers No. 592.
- Cerutti, E., M. Obstfeld and Z. Zhou, 2019. Covered Interest Parity Deviations: Macrofinancial Determinants. IMF Working Paper No. 19/14.
- Ivashina, V., D. Scharfstein and J. Stein, 2015. Dollar Funding and the Lending Behavior of Global Banks. The Quarterly Journal of Economics, Vol. 130, No. 3, pp. 1241–1281.
- Iida, T., T. Kimura and N. Sudo, 2016. Regulatory Reforms and the Dollar Funding of Global Banks: Evidence from the Impact of Monetary Policy Divergence. Bank of Japan Working Paper Series No. 16-E-14.
- Sushko, V., C. Borio, R. McCauley and P. McGuire, 2016. The Failure of Covered Interest Parity: FX Hedging Demand and Costly Balance Sheets. BIS Working Paper No. 590.
- (Full list of references appears in the source PDF.)

### Empirical findings from tables (exact reported coefficients and statistics)
- Table 1 — Subsequent cumulative change in domestic financial conditions in response to a 100-basis point shock to cross-currency basis.
  - Corporate Spread — Change in CCBS by horizon (months 1 to 10): 0.48 1.04 1.22 1.34 1.76 2.10 2.03 1.98 1.66 1.99 (robust s.e. in parentheses: (0.34)(0.52)(0.61)(0.66)(0.70)(0.89)(0.87)(0.87)(0.81)(0.78)). Country FE: YYYYYYYYYY. Controls: YYYYYYYYYY. R_square: 0.146 0.195 0.206 0.209 0.230 0.213 0.211 0.220 0.252 0.286. Number of observations: 1,469 1,463 1,453 1,442 1,430 1,418 1,406 1,394 1,382 1,370.
  - Sovereign Spread — Change in CCBS by horizon (months 1 to 10): 0.62 2.74 3.53 4.36 2.44 3.89 2.48 2.18 3.12 2.60 (s.e.: (0.14)(0.18)(0.21)(0.23)(0.24)(0.27)(0.26)(0.26)(0.26)(0.28)). Country FE and Controls: YYYYYYYYYY. R_square: 0.172 0.171 0.150 0.155 0.149 0.150 0.152 0.167 0.176 0.184. Number of observations: 1,696 1,688 1,676 1,663 1,649 1,635 1,621 1,607 1,593 1,579.
  - Interbank Spread 3M — Change in CCBS by horizon (months 1 to 10): 3.03 5.47 6.17 3.91 5.82 3.81 4.35 2.28 3.22 3.30 (s.e.: (0.29)(0.36)(0.31)(0.31)(0.32)(0.32)(0.33)(0.31)(0.29)(0.30)). Country FE and Controls: YYYYYYYYYY. R_square: 0.216 0.203 0.160 0.154 0.145 0.142 0.142 0.133 0.135 0.140. Number of observations: 1,624 1,616 1,603 1,590 1,576 1,562 1,548 1,534 1,520 1,506.

- Table 2 — Subsequent cumulative change by Net IIP positions (100-basis point shock to cross-currency basis). Regressions run separately for net positive IIP countries and net negative IIP countries. Selected reported sequences (months 1 to 10) with reported robust s.e. in parentheses:
  - Corporate Spread — Net Negative IIP: -5.39 -8.86 -7.55 -8.38 -8.82 -7.35 -6.48 -6.80 -15.09 -7.55 (s.e.: (0.25)(0.31)(0.42)(0.45)(0.54)(0.60)(0.64)(0.67)(0.69)(0.68)). Country FE: YYYYYYYYYY. Controls: (varied). R_square: 0.252 0.247 0.281 0.291 0.278 0.283 0.246 0.219 0.226 0.261. Number of observations: 607 603 598 593 588 583 578 573 568 563.
  - Corporate Spread — Net Positive IIP: 11.25 17.83 20.27 27.29 27.54 29.97 31.43 35.36 34.43 31.83 (s.e.: (0.57)(0.67)(0.81)(0.92)(0.92)(1.19)(1.17)(1.24)(1.16)(1.09)). Country FE: YYYYYYYYY. Controls: YYYYYYYYY. R_square: 0.179 0.240 0.251 0.247 0.268 0.253 0.253 0.268 0.308 0.348. Number of observations: 862 860 855 849 842 835 828 821 814 807.
  - Sovereign Spread — Net Negative IIP: 3.50 4.87 6.60 8.19 2.88 5.09 2.89 1.65 4.48 3.48 (s.e.: (0.26)(0.31)(0.36)(0.39)(0.39)(0.43)(0.43)(0.45)(0.44)(0.48)). Country FE and Controls: reported. R_square: 0.215 0.236 0.220 0.218 0.212 0.213 0.211 0.228 0.241 0.250. Number of observations: 843 841 836 830 823 816 809 802 795 788.
  - Sovereign Spread — Net Positive IIP: -2.46 -1.94 -3.15 -1.64 -2.47 -2.32 -0.81 -0.28 -1.49 -0.53 (s.e.: (0.12)(0.18)(0.14)(0.16)(0.15)(0.17)(0.18)(0.19)(0.19)(0.20)). Country FE and Controls: reported. R_square: 0.244 0.180 0.173 0.179 0.180 0.193 0.184 0.177 0.177 0.183. Number of observations: 853 847 840 823 826 819 812 805 798 791.
  - Interbank Spread 3M — Net Positive IIP: 7.48 12.00 12.28 8.03 11.14 8.21 8.67 5.14 6.68 6.76 (s.e.: (0.43)(0.50)(0.48)(0.47)(0.45)(0.44)(0.48)(0.47)(0.46)(0.47)). Country FE and Controls: reported. R_square: 0.301 0.291 0.244 0.233 0.216 0.210 0.202 0.180 0.179 0.187. Number of observations: 819 817 811 805 798 791 784 777 770 763.
  - Interbank Spread 3M — Net Negative IIP: -2.03 -3.74 -3.10 -3.21 -2.90 -3.33 -2.07 -1.58 -2.13 -1.10 (s.e.: (0.10)(0.16)(0.15)(0.18)(0.21)(0.21)(0.24)(0.26)(0.27)(0.28)). Country FE and Controls: YYYYYYYYYY. R_square: 0.207 0.161 0.156 0.147 0.144 0.145 0.141 0.137 0.137 0.130. Number of observations: 805 799 792 785 778 771 764 757 750 743.

- Table 3 — High-stress vs. Low-stress periods (High-stress defined as VIX > about 35). Subsequent cumulative change to a 100-basis point shock to cross-currency basis, months 1 to 10:
  - Corporate Spread — High Stress: 1.38 3.40 3.98 4.44 5.88 7.87 6.57 5.69 4.59 5.68 (s.e.: (1.17)(1.71)(1.90)(2.02)(2.18)(3.44)(2.89)(2.27)(2.11)(2.17)). Corporate Spread — Low Stress: 0.19 1.17 0.86 0.81 1.07 1.29 1.95 1.98 1.57 1.65 (s.e.: (0.39)(0.49)(0.53)(0.58)(0.67)(0.74)(0.73)(0.79)(0.76)(0.68)). Country FE and Controls: YYYYYYYYYY. R_square: 0.237 0.286 0.304 0.321 0.348 0.345 0.337 0.343 0.409 0.471. Number of observations: 1469 1463 1453 1442 1430 1418 1406 1394 1382 1370.
  - Sovereign Spread — High Stress: 0.95 1.20 1.20 1.05 1.02 0.54 0.94 0.72 0.70 0.83 (s.e.: (0.24)(0.25)(0.26)(0.30)(0.27)(0.28)(0.34)(0.34)(0.37)(0.35)). Sovereign Spread — Low Stress: 0.20 0.22 0.42 0.50 0.16 0.32 0.18 0.13 0.22 0.18 (s.e.: (0.18)(0.25)(0.28)(0.30)(0.30)(0.34)(0.32)(0.34)(0.32)(0.34)). Country FE and Controls: YYYYYYYYYY. R_square: 0.196 0.197 0.171 0.171 0.174 0.179 0.178 0.192 0.201 0.212. Number of observations: 1692 1688 1676 1663 1649 1635 1621 1607 1593 1579.
  - Interbank Spread 3M — High Stress: 0.95 1.20 1.20 1.05 1.02 0.54 0.94 0.72 0.70 0.83 (s.e.: (0.43)(0.62)(0.59)(0.59)(0.60)(0.54)(0.46)(0.44)(0.47)(0.41)). Interbank Spread 3M — Low Stress: 0.00 0.18 0.42 0.24 0.37 0.19 0.04 -0.14 0.13 0.02 (s.e.: (0.15)(0.20)(0.31)(0.31)(0.28)(0.33)(0.39)(0.36)(0.28)(0.32)). Country FE and Controls: YYYYYYYYYY. R_square: 0.305 0.293 0.237 0.211 0.209 0.212 0.207 0.194 0.191 0.190. Number of observations: 1624 1616 1603 1590 1576 1562 1548 1534 1520 1506.

- Table 4 — High-stress vs. Low-stress periods, by Net IIP positions. Selected sequences (months 1 to 10):
  - Corporate Spread — Net Positive IIP: High Stress: -0.50 -1.03 -0.83 -0.94 -1.71 -1.58 -1.34 -2.18 -4.18 -4.27 (s.e.: (0.59)(0.80)(0.93)(0.80)(0.96)(0.73)(0.83)(1.14)(0.78)(0.74)). Low Stress: 0.15 -0.08 -0.18 -0.19 -0.24 -0.32 -0.31 -0.57 -0.77 -0.71 (s.e.: (0.30)(0.42)(0.47)(0.42)(0.37)(0.34)(0.42)(0.46)(0.42)(0.46)). R_square: 0.580 0.578 0.586 0.655 0.712 0.769 0.755 0.738 0.759 0.757. Number of observations: 607 603 598 593 588 583 578 573 568 563.
  - Corporate Spread — Net Negative IIP: High Stress: 3.39 6.05 8.74 13.91 10.32 12.25 11.51 11.33 12.35 10.43 (s.e.: (3.33)(3.21)(4.02)(4.13)(3.71)(6.42)(5.11)(3.59)(3.45)(3.76)). Low Stress: 0.01 0.94 0.33 0.13 0.24 0.57 1.24 0.92 0.21 0.09 (s.e.: (0.59)(0.73)(0.74)(0.81)(0.96)(1.12)(1.11)(1.13)(1.12)(1.04)). R_square: 0.312 0.372 0.417 0.469 0.493 0.482 0.469 0.480 0.584 0.667. Number of observations: 807 860 855 849 842 835 828 821 814 (as reported).
  - Sovereign Spread — Net Positive IIP: High Stress: -0.87 -0.94 -0.10 -0.41 -0.60 -0.51 -0.32 -0.69 -0.88 -0.38 (s.e.: (0.32)(0.38)(0.48)(0.42)(0.46)(0.56)(0.65)(0.71)(0.78)(0.76)). Low Stress: -0.21 -0.46 -0.23 -0.46 -0.27 -0.51 -0.22 -0.04 -0.08 -0.29 (s.e.: (0.28)(0.32)(0.37)(0.40)(0.42)(0.41)(0.43)(0.45)(0.47)(0.50)). R_square: 0.583 0.487 0.437 0.461 0.464 0.485 0.478 0.493 0.509 0.526. Number of observations: 487 484 480 476 472 468 464 460 456 452.
  - Sovereign Spread — Net Negative IIP: High Stress: 1.51 0.87 1.46 2.25 2.29 3.25 3.17 2.82 3.78 3.53 (s.e.: (1.19)(0.91)(0.87)(1.13)(1.16)(1.34)(1.45)(1.59)(1.87)(1.90)). Low Stress: 0.34 0.54 0.78 0.87 0.25 0.45 0.15 0.06 -0.11 -0.22 (s.e.: (0.27)(0.40)(0.44)(0.48)(0.48)(0.56)(0.53)(0.60)(0.56)(0.59)). R_square: 0.255 0.275 0.254 0.246 0.243 0.247 0.248 0.267 0.288 0.302. Number of observations: 843 841 836 830 823 816 809 802 795 788.
  - Interbank Spread 3M — Net Positive IIP: High Stress: -0.10 -0.56 -0.35 -0.39 -0.11 -0.20 0.11 0.21 0.24 0.45 (s.e.: (0.31)(0.40)(0.40)(0.43)(0.47)(0.53)(0.58)(0.60)(0.64)(0.68)). Low Stress: -0.11 0.03 -0.10 -0.10 -0.06 -0.06 0.03 0.17 0.16 0.18 (s.e.: (0.12)(0.12)(0.17)(0.16)(0.17)(0.18)(0.19)(0.20)(0.21)(0.23)). R_square: 0.278 0.252 0.262 0.257 0.260 0.267 0.276 0.277 0.280 0.273. Number of observations: 805 799 792 785 778 771 764 757 750 743.
  - Interbank Spread 3M — Net Negative IIP: High Stress: 2.67 3.85 2.73 2.18 3.92 1.27 2.15 1.43 1.49 1.32 (s.e.: (0.53)(0.56)(0.96)(0.83)(0.76)(0.76)(0.64)(0.77)(0.95)(0.88)). Low Stress: 0.15 0.29 0.72 0.66 0.48 0.14 0.14 -0.24 0.14 0.01 (s.e.: (0.25)(0.31)(0.45)(0.44)(0.39)(0.47)(0.55)(0.53)(0.47)(0.52)). R_square: 0.440 0.446 0.382 0.336 0.329 0.320 0.293 0.274 0.275 0.269. Number of observations: 819 817 811 805 798 791 784 777 770 763.

### Annex — A Simple Model of FX Hedging Demand (model structure, equations, and key implications)
- Purpose: present a simple model of a non-U.S. bank’s demand for FX swaps to understand implications of a higher basis and FX hedging costs on domestic financial conditions. Model based on Ivashina et al. (2015) and Iida et al. (2018).
- Agents and assets:
  - Bank invests in USD-denominated assets (loans and bonds, etc.) issued by borrowers in the United States (L_USD), and local currency (LCU)-denominated assets (L_L).
  - Expected gross returns: g_L(·) and g_USD(·), concave functions of amounts of loans.
  - Aggregate lending constraint: L_USD + L_L ≤ K.
  - Default probability: p; size of default: α.
- Funding sources (liabilities): LCU-denominated funding (D_L), USD-denominated funding (D_USD), and FX swaps (S).
- Cost functions (as specified in the source):
  - g_L(L_L) = (1 + q) L_L − τ_L 2 (L_L)^2
  - g_USD(L_USD) = (1 + q_USD) L_USD − τ_USD 2 (L_USD)^2
  - 표표_L(D_L) = (1+r) D_L + η_L 2 m m m m( D_L )^2  (as printed in source)
  - 표표_USD(D_USD) = (1+ r_USD + p α) D_USD + η_USD 2 (D_USD)^2
  - FX swap funding cost = (r_USD − r − ∆) S, where ∆ is the cross-currency basis (when CIP holds, ∆ = 0).
  - Minimum liquidity needs: V.
- Bank’s profit maximization problem (as printed in source):
  - max_{L_L, L_USD, D_L, D_USD, S} { g_L(L_L) + g_USD(L_USD) − 표표_L(D_L) − 표표_USD(D_USD) − (r_USD − r − ∆) S } subject to M ≥ V; L_L = D_L − S; L_USD + M = D_USD + S.
- First-order conditions and symmetry assumption (τ_L = τ_USD and η_L = η_USD). Optimal asset holdings (as given exactly in the source):
  - Equation (1):
    L_USD =
    1
    τ + η
    {�1+ η 2 τ � (q_USD − r_USD) − η 2 τ (q − r) − τ + η 2 τ ∆ − p 2 α − η 2 V }
  - Equation (2):
    L_L =
    1
    τ + η
    {�1− η 2 τ � (q − r) − η 2 τ (q_USD − r_USD) + τ + η 2 τ ∆ − p 2 α − η 2 V }
  - (Equations are reproduced verbatim as in the source PDF.)
- Comparative-static implications (explicitly stated in the source):
  - An increase in cross-currency basis (∆) will lead banks to increase their assets in local currency, and decrease U.S. dollar-denominated assets. (Consistent with Ivashina et al. (2017).)
  - An increase in the U.S. policy rates (q_USD ↑, holding other terms constant) leading to wider interest rate differentials will, ceteris paribus, lead banks to invest more in U.S. dollar-denominated assets and less in local-currency-denominated assets.
- Net liabilities (Net Negative IIP) case implications (as described in the source):
  - If the bank has a net liabilities position vis-à-vis the U.S. dollar and domestic financing cost (η_L) is very high, the bank relies heavily on USD funding (D_USD). An increase in cross-currency basis (∆) reduces FX swap funding (S), and domestic financing cannot be raised to offset the reduction. The reduction in total liabilities forces the bank to reduce overall assets, which tightens domestic financial conditions.
- Joint movements (discussion in the source):
  - If both U.S. rates and the cross-currency basis rise simultaneously (as suggested by Avdjiev et al. 2017), the net effect on bank portfolio choices is ambiguous: higher U.S. rates incentivize more USD asset investment, but higher FX hedging costs (∆) lower hedge-adjusted USD returns. Net effect depends on parameter values.

*Source: wpiea2019169-print-pdf - REFERENCES (source PDF content reproduced exactly for reported figures and equations).*

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