## Dataset: Chapter 5. Banks' Dollar Funding: A Source of Financial Vulnerability

## Source details

**Canonical URL:** [Dataset: Chapter 5. Banks' Dollar Funding: A Source of Financial Vulnerability](https://www.imf.org/-/media/files/publications/gfsr/2019/october/english/data/gfsroct2019-ch5.xlsx)

## Other formats

- [Markdown version](/-/media/files/publications/gfsr/2019/october/english/data/gfsroct2019-ch5.xlsx.md)
- [Structured JSON version](/-/media/files/publications/gfsr/2019/october/english/data/gfsroct2019-ch5.xlsx.json)

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### Dataset scope and structure
- Worksheet count and notable sheets:
  - "GFSR Chapter 5 Oct. 2019" — 32 rows, 10 columns.
  - "Table of Contents" — 25 rows, 12 columns (references title: "Chapter 5. Banks' Dollar Funding: A Source of Financial Vulnerability").
  - Figure worksheets include: "Figure 5.1.", "Figure 5.2.", "Figure 5.3.", "Figure 5.4.", "Figure 5.5.", "Figure 5.6.", "Figure 5.7.", "Figure 5.8.", "Figure 5.9", "Figure 5.10." with varying row/column counts and time series / panel data.
- Data types present:
  - Time series dates (examples: "2000-01-01T00:00:00.000Z", "2012-01-02T00:00:00.000Z", "1999-04-01T00:00:00.000Z").
  - Cross-sectional country identifiers (examples: "LUX", "ZAF", "FRA", "CAN", "AUT", "JPN").
  - Monetary magnitudes in trillions of US dollars and percentages.
  - Percentile and distributional statistics (25th percentile, Median, 75th percentile).
  - Coefficients and standardized regression outputs (example coefficient values given for regulation effects).

### Key empirical measures and exact statistics (sample values preserved verbatim)
- Figure 5.1 — Trends in US Dollar Activities of Non-US Banks:
  - 2000-01-01: 3.3361675; 2.547637; 0.6372361875; 0.163940484375.
  - Country snapshot entries (examples):
    - "LUX": 1.004014253616333; 0.5661126375198364; 31.448196411132812; 0.
    - "ZAF": 6.096949577331543; 7.895219802856445; 7.40151834487915; 0.
    - "FRA": 9.112838745117188; 7.439943790435791; 8.063249588012695; 0.4788779318332672.
  - Total assets example: 79974.1171875; 2500.81396484375; 11187.9912109375.

- Figure 5.2 — US Dollar Funding Fragility of Non-US Banks:
  - Aggregate reference values: LR 2018 = 108.37499216765659; SFR 2010 = 114.89512884976844.
  - Example time-series points: 2000-01-01: 217006.25; 6.8350725173950195.
  - Components and percentile ranges (examples): 25th percentile; Median; 75th percentile.
  - Example country ratios: "CAN" = 43.453343164478724; "AUT" = 44.54608469321319; "JPN" = 45.173301912456196.

- Figure 5.3 — The Cross-Currency Basis:
  - Distributional basis points sample (monthly averages): 2000-01-01: -8.537454; -2.727748; 7.618393.
  - Regression / interaction entries (examples labeled "Low CCFR" and "High CCFR"):
    - Low CCFR coefficients: 0.2752449; 0.0864244; 0.0693581; -0.0159034; -0.1558699; -0.1063343; -0.2238812.
    - High CCFR coefficients: 0.5102703; -0.01047; 0.5154408; -0.2169469; -0.3028802; -0.1448445; -0.4108232.

- Figure 5.4 — Financial Regulations and the Cross-Currency Basis:
  - One-month and three-month CCB example values around 2012-01:
    - 2012-01-02: One-month CCB = -27.456628799438477; Three-month CCB = -31.482059478759766.
  - Coefficients for pre/post regulation labels (examples):
    - "Prereform": -0.068; -0.057.
    - "Stress VaR": -0.044000000000000004; -0.031000000000000003.
    - "Supplementary leverage ratio": -0.060000000000000005; -0.05.
    - "Liquidity coverage ratio": -0.10900000000000001; -0.087.

- Figure 5.5 — US Dollar Funding and Financial Stress:
  - Change-in-default and change-in-FCI sample coefficients (All / 2007-09 / 2011-12):
    - ∆PD: 0.219; 0.328; 0.469.
    - ∆FCI: 0.286; 0.398; 0.579.

- Figure 5.6 — Spillovers from US Dollar Funding Costs to Cross-Border Lending:
  - Change in cross-border lending following a 50 basis point increase in US dollar funding costs (Percent):
    - Whole sample: -5.309.
    - All lenders to EMs: -6.621.
    - All lenders to AEs: -3.572.
    - EMs to all recipients: -7.057.
    - EMs to EMs: -9.274.

- Figure 5.7 — Amplification Effects (sample impacts on probability of default):
  - ∆PD: 0.154893298; 0.31984288000000005.

- Figure 5.8 — Subsidiary-Branch Mix (US dollar liquidity ratio of US subsidiary):
  - High / Low sample values: 0.0262391; 0.1049564.

- Figure 5.9 — Mitigating Effect of Home Economy Bank Health:
  - Low bank-health group (Capital; Liquidity; ROA): 0.4004142; 0.5410608; 0.4478675.
  - High bank-health group (Capital; Liquidity; ROA): 0.2492171; 0.2372877; 0.2653571.

- Figure 5.10 — Swap Lines and Reserve Holdings (cross-currency basis distribution quantiles):
  - p50-p75 sample values:
    - "Currencies without swap lines" Before = -52.999481201171875; After = -42.30262565612793.
    - "Currencies with swap lines" Before = -8.783451080322266; After = -9.147095441818237.
  - p25-p50 sample values:
    - "Currencies without swap lines" Before = -31.589675903320312; After = -17.029571533203125.
    - "Currencies with swap lines" Before = -37.435386657714844; After = -10.548763275146484.

### Policy-relevant variables and analytical signals present in the dataset
- Measures of non-US banks’ US dollar exposures and claims (levels in trillions of US dollars and country-specific shares).
- Cross-currency funding gap and cross-currency funding ratio (aggregate indicators with reference LR 2018 = 108.37499216765659 and SFR 2010 = 114.89512884976844).
- US dollar liquidity and stable funding ratios (percentiles, medians, and decompositions across funding sources).
- Cross-currency basis (one-month and three-month CCB) and standardized regression coefficients linking CCFR, term spread differential, US dollar index, VIX, LIBOR-OIS spread, bid-ask spread, and FX implied volatility to basis movements.
- Indicators of financial stress transmission:
  - ∆PD and ∆FCI relationships with US dollar funding costs (sample values provided).
  - Cross-border lending sensitivity to a 50 basis point increase in US dollar funding costs (sample percent impacts provided).
- Institutional mitigation variables:
  - Effects of financial regulations (leverage, liquidity, stress VaR) on the cross-currency basis (sample coefficients provided).
  - Mitigating roles of swap lines and international reserve holdings (distributional shifts in cross-currency basis quantiles provided).
  - Bank health dimensions (Capital, Liquidity, ROA) and their differential mitigation magnitudes.

### Uses and potential analytical queries supported by the dataset (examples implied by content)
- Time-series analysis of non-US banks’ US dollar claims and components from 2000 onward.
- Cross-country comparisons of branch/subsidiary mixes, share of US dollar claims, and total assets.
- Regression and decomposition analyses linking cross-currency basis movements to funding ratios, market volatility, and regulation dummies.
- Stress-testing or scenario simulations using quantified sensitivities (for example, the percent decline in cross-border lending after a 50 basis point funding shock).
- Evaluating policy interventions (leverage regulation, liquidity coverage ratio, swap line ceilings) using pre/post coefficient and distributional comparisons.

*Dataset: gfsroct2019-ch5 (figures and time-series panels for Chapter 5: Banks' Dollar Funding).*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2019/october/english/data/gfsroct2019-ch5.xlsx_