## GFSR Chapter 1 Oct. 2022 — Dataset overview (ch1data)

## Source details

**Canonical URL:** [GFSR Chapter 1 Oct. 2022 — Dataset overview (ch1data)](https://www.imf.org/-/media/files/publications/gfsr/2022/october/data/ch1data.xlsx)

## Other formats

- [Markdown version](/-/media/files/publications/gfsr/2022/october/data/ch1data.xlsx.md)
- [Structured JSON version](/-/media/files/publications/gfsr/2022/october/data/ch1data.xlsx.json)

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### Dataset scope and structure
- Worksheet count: multiple figures and tables tied to Chapter 1 ("Navigating the High-Inflation Environment").
- Representative worksheet names include: "GFSR Chapter 1 Oct. 2022", "Table of Contents", "Figure 1.1.", "Figure 1.2.", ... up through "Figure 1.3.2".
- Example worksheet dimensions (sample):
  - "GFSR Chapter 1 Oct. 2022": rowCount 32, columnCount 10.
  - "Table of Contents": rowCount 43, columnCount 12.
  - "Figure 1.1.": rowCount 2939, columnCount 28.
  - "Figure 1.12.": rowCount 4355, columnCount 30.

### Major themes and analytic content (by figure headings)
- Global financial conditions and regional financial-condition indices (Figure 1.1. — Advanced economies and Emerging markets).
- Sell-off in risk assets and jump in volatility (Figure 1.2.).
- Crypto market instability, including stablecoin market capitalization (Figure 1.3.).
- Equity returns decomposition and repricing of economic risks (Figure 1.4.).
- Large currency moves in advanced economies and emerging markets (Figure 1.5.).
- European energy crisis and fragmentation/fiscal concerns (Figure 1.6.).
- Growth-at-Risk and macro-financial vulnerability metrics (Figure 1.7.; Figure 1.1.1.).
- Drivers of bond yields, inflation expectations, and policy rates versus neutral levels (Figures 1.8.–1.10.).
- Historical record of monetary policy tightening and recessions (Figure 1.11.; Figure 1.3.1; Figure 1.3.2).
- Emerging market monetary policy outlook, term-premium changes, volatility in local yields (Figure 1.12.).
- Emerging market portfolio flows, new issuance, capital flows at risk (Figure 1.13.).
- Emerging market vulnerabilities: macro indicators, public debt currency composition, reserve adequacy, sovereign spreads (Figure 1.14.).
- Frontier market access and debt vulnerabilities (Figure 1.15.).
- China property sector indicators and related banking/firm metrics (Figure 1.16.).
- Market liquidity conditions and market structure (Figures 1.17.–1.18.).
- Corporate performance, default outlook, leveraged finance trends, CLO investor base, LBO volumes (Figures 1.19.–1.20.).
- Housing sector trends, price-to-income, house-price-at-risk projections (Figure 1.21.).
- Tightening bank lending standards and contributing factors (Figure 1.22.).
- Macro-financial scenarios (baseline and adverse) and stress-testing impacts on banks (Figures 1.23.–1.24.).

### Selected exact sample statistics and time series (preserved as in source)
- Figure 1.1. (Financial Conditions, sample rows):
  - 2006-03-31: United States = -0.9653285666519741; Euro area = -1.1810473668605397; Other advanced economies = -1.2973597788301485.
  - 2006-06-30: United States = -0.49472224987395036; Euro area = -0.9297147528138203; Other advanced economies = -1.338905636129282.
- Figure 1.3. (Stablecoin Market Capitalization, (billions of US dollars)):
  - 2020-01-01: Tether = 4.285941694738991; Cash equivalent backed, US registered = 0.7463183162516456; Crypto backed or algorithmic = 0.19627170419481071.
  - 2020-01-05: Tether = 4.287220840862627; Cash equivalent backed, US registered = 0.7718224506927562; Crypto backed or algorithmic = 0.1943088031634057.
- Figure 1.4. (S&P 500 equity index returns decomposition, percent):
  - Oct. 2021 sample row: Price returns = -0.8229978954131023; Risk-free rate = -1.598359769583646; Earnings = -0.0157441057909935; Equity risk premiums = 0.7911059799615372.
- Figure 1.5. (Emerging Market Regional Currencies, percent appreciation/depreciation vs US dollar):
  - 2022-01-03: EM FX = 0.03234029600882593; Latin America = 0.04385879417554417; CEEMEA = -0.23937890877722134; Asia = -0.015686889681942873.
  - 2022-01-05: EM FX = -0.5611992542708277; Latin America = 1.0097185409567055; CEEMEA = -0.16940922578905226; Asia = -0.030258203335120637.
- Figure 1.12. (Changes in five-year term premiums since January 2020; sample dates):
  - 2019-10-10: Asia ex China = -22.983333333333334; CEE = -23.853333333333328; LATAM = -13.375000000000009; US = -22.970000000000002.
  - 2010-01-31 (Standard deviation of 20-day moving average in emerging market local yields): US = 3.520173155914218; Asia = 6.057162623507744; Latam = 5.117692979123645; CEE = 3.4640293081351117.
- Figure 1.13. (Emerging Market Portfolio Flows, sample):
  - 2022-01-31: Cumulative EM Bonds (excluding CHN) = 10.359924078534227; Cumulative EM Equities (excluding CHN) = 0.09528099999999982; China Equities = 3.281830623313976; China Bonds = 7.874457649500052.
  - Quarterly capital flows at risk series (example): 18Q1 = 0.21172641542830015; Latest (Q3) = 0.4162783444070901.
- Figure 1.14. (Emerging Market Vulnerabilities, Macroeconomic Indicators, selected percentiles and values):
  - Current Account Deficit: Latest = 22.55944510273383? [note: worksheet shows "Sovereign" 22.55944510273383 under Figure 1.1.1; Figure 1.14 sample includes many percentile/indicator values such as 21.7, 95.6, 60.8, 54.99999999999999].
  - Fiscal Deficit: sample values include 100, 91.3, 73.9, 52.1 in the table header rows.
- Figure 1.15. (Frontier market hard-currency spreads and debt indicators, samples):
  - Debt-to-GDP median/time series examples: 2010 median = 8.370774244044135; 2021 median = 19.77482441517273; 2022 (Q4) sample index labels include 23..29.
  - Examples of spreads buckets and country-specific entries: "more than 1,000 basis points" = 3.0111583289999997; "Traded above 1000 bps" sample country "BHS" entries: 59.5, 14.8, 25.1.
- Figure 1.16. (China property sector, sample series and indices):
  - Reference: December 2011; May-2019 reference value = 1; Residential real estate sales and financing series include values such as 43586, 43617, 43647, 43678, 43709 in sample rows.
  - "Firms with EBIT less than net interest expense" = 8.70793316168257 (sample).
- Figure 1.21. (Housing sector trends, selected country values):
  - TUR 2019:Q4–2021:Q4 = 45.781423771898886; NZL = 35.25153618041285; USA = 19.312922047128374.
  - Price-to-income example: ESP = 78.99576; PRT = 94.70259; LUX = 84.35706.
- Figure 1.22. (Tightening bank lending standards, sample time series):
  - LOAN DEMAND (USA) 2007-01-01 = -0.026540662208040505; JPN = 1.047883712198745; EUR = 1.1323314559490103; EM = 0.1764668804046851.
  - LOAN STANDARDS (USA) 2007-01-01 = 0.10367749496996483; JPN = -0.18126990523387615; EUR = -0.121272020973031; EM = 0.5653221142650975.
- Figure 1.23. (Macro-Financial Scenario: Real GDP Growth, percent — Baseline vs Adverse samples):
  - AE Baseline: 2021 = 5.10158616962842; 2022 = 2.393504436886324; 2023 = 0.9341563524500753; 2024 = 1.4667279032242184.
  - AE Adverse: 2021 = 5.10158616962842; 2022 = 0.6361336277257305; 2023 = -3.433184464913639; 2024 = 1.8548238001791342.
  - EM Baseline (sample columns): 2021 = 6.831154656520829; 2022 = 5.261869074647596; 2023 = 4.026615045989836; 2024 = 4.520963856398699.
  - EM Adverse: 2021 = 6.831154656520829; 2022 = 1.5500028932535308; 2023 = -2.0146793983154234; 2024 = 5.101316167982612.
- Figure 1.24. (Impact on banks’ capital ratios, percent — Baseline vs Adverse samples):
  - AE Baseline capital ratio series: 2021 = 13.952046982766465; 2022 = 12.916416409344677; 2023 = 13.522906786049221; 2024 = 14.067890401738248.
  - AE Adverse: 2021 = 13.952046982766465; 2022 = 12.146053820783518; 2023 = 11.355751432313694; 2024 = 11.577396213108102.
  - EME Baseline: 2021 = 14.95062185575931; 2022 = 14.139902063557816; 2023 = 15.325912575374629; 2024 = 16.213356760377128.
  - EME Adverse: 2021 = 14.95062185575931; 2022 = 13.381853699997615; 2023 = 12.30697277800224; 2024 = 10.665073290811188.
  - All Baseline: 2021 = 14.056485938248697; 2022 = 13.036981306080254; 2023 = 13.702470261092902; 2024 = 14.281611192679621.
- Figure 1.1.1. (Global Financial Vulnerabilities — proportion of economies with elevated vulnerabilities, percent of countries):
  - Sovereign: Latest = 22.55944510273383; Previous = 53.848882975627944; GFC = 76.18162376513673.
  - Companies: Latest = 50.20619084455771; Previous = 65.02970806793961; GFC = 40.82671066182905.
  - Households: Latest = 62.16827459534326; Previous = 40.38027381449022; GFC = 45.39151764855811.
  - Banks: Latest = 73.66411297270903; Previous = 48.51263855448431; GFC = 35.516326234529686.
  - Insurers: Latest = 64.37579374530709; Previous = 62.760432568699244; GFC = 66.37554430026573.

### Use cases and analytic notes
- Time series and cross-sectional data suitable for:
  - Reconstructing chart series for Figures 1.1–1.24 and annex figures (1.1.1, 1.2.1, 1.3.1, 1.3.2).
  - Running scenario analysis using the provided baseline and adverse GDP paths and bank stress-test inputs.
  - Examining regional/sectoral vulnerabilities via the percentile and index measures reported for EMs, frontier markets, China property, housing, corporate and bank metrics.
- Numeric precision: the dataset provides high-precision numeric entries (many values recorded to many decimal places) and exact dates (ISO 8601 timestamps such as "2006-03-31T00:00:00.000Z", "2020-01-01T00:00:00.000Z", etc.) which should be preserved in analysis.

*Dataset canonical URL: https://www.imf.org/-/media/files/publications/gfsr/2022/october/data/ch1data.xlsx*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2022/october/data/ch1data.xlsx_