## gfsr-oct-2019-ch3 — Dataset overview

## Source details

**Canonical URL:** [gfsr-oct-2019-ch3 — Dataset overview](https://www.imf.org/-/media/files/publications/gfsr/2019/october/english/data/gfsr-oct-2019-ch3.xlsx)

## Other formats

- [Markdown version](/-/media/files/publications/gfsr/2019/october/english/data/gfsr-oct-2019-ch3.xlsx.md)
- [Structured JSON version](/-/media/files/publications/gfsr/2019/october/english/data/gfsr-oct-2019-ch3.xlsx.json)

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### Dataset scope and primary theme
- Title (worksheet): "GFSR Chapter 3 Oct. 2019"
- Chapter theme (from workbook): "Chapter 3. Institutional Investors: Falling Rates, Rising Risks"
- Dataset contains figure-level data and time-stamped series used to generate charts and tables for Chapter 3 of the Global Financial Stability Report (October 2019).

### Figure 3.1 — Institutional Investors' Increased Risk-Taking
- Captions and subpanels:
  - "1. Market Capitalization and Share of Negative Yielding Global Bonds"
  - "2. Fixed-Income Funds: Low-Rated Portfolios by Credit Quality"
  - "3. Fixed-Income Funds: Effective Maturity"
  - "4. Pension Funds: Long-Term Interest Rates and Defined-Benefit Pension Liabilities"
  - "5. Large Pension Funds: Alternative Asset and Cash Allocations"
  - "6. Life Insurers: Guaranteed Return Spreads and Duration Mismatches"
- Selected datapoints preserved exactly as in the source:
  - Market capitalization (trillions of US dollars): 2507668.72 (associated timestamp "2015-03-31T00:00:00.000Z")
  - Share of total global bond market (percent): 5.836740806326112
  - Fixed-income funds (category "B-BB"): 13.495849505594693 (sample row)
  - High-yield funds: 7.931520721271919 (associated timestamp "2014-12-31T00:00:00.000Z")
  - Ten-year government bond yield: 7.931520721271919 and 6.7054418452238105 (from same row)
  - Pension liabilities (index, end-2013 = 100): 99.99999999999999 (associated timestamp "2013-12-31T00:00:00.000Z")
  - Interquartile (min): 6.444826609064366, 8.933115831432922, 7.569490783653512, 7.827007809988467, 9.238386911849814, 9.843551669746422, 9.594887693381992, 10.621728779135548, 10.718979727107156, 11.442737601737184, 11.934747590313734
  - Interquartile (max): 16.433360757486632, 20.485308541078574, 20.896436918692093, 26.20530565167244, 29.463202720130514, 29.97513254316291, 29.144973331898534, 29.36602453196994, 30.85665629776661, 31.970883512465743, 33.659844976942196
  - Country sample entries: "USA" with value -0.8269000000000002 and "JPN" with value -2.344

### Figure 3.2 — Fixed-Income Funds Risks and Increasing Portfolio Similarities
- Caption highlights:
  - "4. Holdings of US Corporate Credit by Global Fixed-Income Funds"
- Selected datapoints preserved exactly:
  - Dec. 2018: US investment-grade corporate debt = 14.171174866356736; US high-yield corporate debt = 22.69500697075706
  - Mar. 2016: US investment-grade corporate debt = 15.7045603276126; US high-yield corporate debt = 15.260669384128189

### Figure 3.3 — Pension Fund Risk-Taking and Countercyclical Investment Capacity
- Subpanels:
  - "1. Pension Funds: Notional Derivatives to Net Assets and Borrowing, 2011 and 2018"
  - "2. Netherlands: Pension Sector Interest Rate Hedge Sensitivity and 10-year German Government Bond Yield"
  - "3. Private Capital Markets: Total Investor Capital Calls and Capital Distributions"
  - "4. Global Pension Funds: Estimated Contingent Obligations as a Percentage of Cash and Fixed-Income Assets, by Total Assets under Management"
- Selected datapoints preserved exactly:
  - Derivatives to net assets (example rows): 2011 = 0.20796779558541464; 2018 = 0.11700925553777795
  - Country example (Canada): 2011 = 77.22598925981836; 2018 = 85.85438032552682
  - Private capital markets (sample): Capital called up = 113.91665354000175; Capital distributed = 91.4484011511603; Net cash flow = -22.46825238884145
  - Pension fund asset buckets (example): "0–10" category values 0.20796779558541464 and 0.11700925553777795

### Figure 3.4 — Asian Life Insurers and Increased Cross-Border Portfolio Allocation
- Subpanels:
  - "1. Foreign Cross-Border and US Dollar Credit Investment"
  - "2. Life Insurers' Assets and Size of Domestic Corporate Bond Markets"
  - "3. Excess Returns on US Investment-Grade Corporate Bonds over Domestic Sovereign Bonds"
  - "4. Shareholders Equity and Foreign Investment"
- Selected datapoints preserved exactly:
  - Life insurer assets (JPN, 2013): 3.4998450177773726 (Total) ; US dollar credit = 0.150977
  - Ratios and examples: "Ratio" = 23.181312503079095 (JPN 2013 entry)
  - Time series example (capital markets timestamps): "2010-01-29T00:00:00.000Z" with values 295.754 and 276.09400000000005 and 62.094000000000094
  - Country/period labels preserved: "JPN 2013" with 7.052186177715091 and 17.4; "JPN 2018" with 9.82318271119843 and 24.2; "KOR 2013" with 8.4 and 5

### Figure 3.5 — Life Insurers from Taiwan Province of China
- Subpanels:
  - "1. Life Insurer from Taiwan Province of China: Share of Sectors of US Dollar Credit"
  - "2. Life Insurers from Taiwan Province of China: Foreign Currency Volatility Reserves and New Taiwan Dollar/US Dollar Exchange Rate"
- Selected datapoints preserved exactly:
  - Sector shares (example): "US corporates" and "Other" with numeric sample 2987.937608323422 in an unlabeled column
  - TWD/USD dated entries: "2015-12-01T00:00:00.000Z" with TWD/USD = 32.862 and FX volatility reserves (average, billions of new Taiwan dollars) = 9.48398725
  - Other timestamps and volatility reserves: "2016-03-16T00:00:00.000Z" TWD/USD = 32.212; FX volatility reserves = 8.308006875; "2016-06-16T00:00:00.000Z" TWD/USD = 32.258; FX volatility reserves = 7.7570465
  - Example share: "TWN (4%)" = 111.25986666666664

### Figure 3.1.1 — Liquidity Stress Scenarios for Fixed-Income Funds
- Subpanels and indicators:
  - "1. Fund Liquidity Indicators" — HQLA, AQLA, Cash
  - "2. Asset Share of Funds with HQLA Shortfalls"
  - "3. Average Liquidity Shortffalls"
  - "4. Frequency of HQLA Shortfalls"
  - "5. Liquidty Sortfalls by Fund Sizes"
  - "6. Liquidty Shortfalls by Region"
- Selected datapoints preserved exactly:
  - HQLA / AQLA / Cash sample (2017-03-31): HQLA = 45.1727273434483; AQLA = 46.9871432215695; Cash = 7.81221474036035
  - Fixed-income funds (Jun. 30, 2017): fixed-income funds = 11.5850656437194; high-yield funds = 48.026465522942004
  - HQLA for funds with liquidity shortfalls (2017-03-31): 1.25959886629488 and 10.2905120168863
  - Asset-size liquidity shortfall examples:
    - Assets ≥ $10 bn (AQLA) shortfall indicator values: 0.432272156222972, 1.66217343906262, 2.05366744136994 (grouped row)
    - Assets ≥ $10 bn (cash) shortfall indicator values: 4.95006027493888, 8.02387426980801, 10.5073095795026
  - Regional AQLA and cash examples (2017-06-30): EA AQLA = 1.25431742525031; US AQLA = 0.513056504656194; EA cash = 11.166152343068; US cash = 5.50143913459601
  - Frequency bucket example: 4 and 8 reported as X-axis categories for shortfall frequency

### Data structure notes
- Worksheets included: "GFSR Chapter 3 Oct. 2019", "Table of Contents", "Figure 3.1.", "Figure 3.2.", "Figure 3.3.", "Figure 3.4.", "Figure 3.5.", "Figure 3.1.1."
- Many numeric cells include timestamps in ISO 8601 format (examples: "2015-03-31T00:00:00.000Z", "2014-12-31T00:00:00.000Z", "2017-03-31T00:00:00.000Z", "2010-01-29T00:00:00.000Z", "2015-12-01T00:00:00.000Z")
- Numeric precision from source is preserved exactly (examples shown throughout the dataset excerpts above)

_Italic: Data extracted from the workbook "gfsr-oct-2019-ch3" (Figure and worksheet extracts as provided in the dataset)._

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2019/october/english/data/gfsr-oct-2019-ch3.xlsx_