## boxfigure1-4-1

## Source details

**Canonical URL:** [boxfigure1-4-1](https://www.imf.org/-/media/files/publications/gfsr/2018/oct/ch1/pdf/boxfigure1-4-1.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/gfsr/2018/oct/ch1/pdf/boxfigure1-4-1.pdf.md)
- [Structured JSON version](/-/media/files/publications/gfsr/2018/oct/ch1/pdf/boxfigure1-4-1.pdf.json)

---

### Summary findings
- The proportion of overall price variation explained by jumps is at historical lows.
- Infinite activity jumps tend to be prevalent in stress episodes and tend to be characteristic of relatively illiquid sectors.
- Measures and results are produced using two methods: "Method 1" (Huang and Tauchen (2005) and Andersen and others (2006)) and "Method 2" (Ait-Sahalia and Jacod (2012)).

### Panel 1 — Proportion of Variation Explained by Jumps
- Metric: Percent; monthly and six-month moving average.
- Two methods shown:
  - Method 1
  - Method 2
- Visual range and ticks shown: 0, 40, 5, 10, 15, 20, 25, 30, 35 (these labels appear on the figure axes).

### Panel 2 — Frequency of Jumps
- Metric: Percent; monthly and six-month moving average.
- Frequency of jumps is based on Method 1.
- Variation explained by jumps (for comparison) is based on Method 2.
- Visual range and ticks shown: 0, 40, 5, 10, 15, 20, 25, 30, 35 (these labels appear on the figure axes).

### Panel 3 — Type of Jumps over Time and during Specific Stress Episodes
- The black and gray lines measure the type of jump:
  - Finite activity (that is, news-related shocks)
  - Infinite activity (that is, a series of small jumps reflecting insufficient liquidity)
- Size of circles depicts the level of the Chicago Board Options Exchange Volatility Index (VIX) on selected stress event days.
- Selected stress events labeled: VIX tantrum, U.S. election, Brexit, Flash crash, Taper tantrum, Black Monday, Flash rally.
- Years on the timeline: 2009 10 11 12 13 14 15 16 17 (presented as a continuous sequence in the figure).
- Additional numeric axis markers presented: 0, 20, 40, 60, 80, 100, 120.

### Panel 4 — Type of Jumps versus Sectoral Liquidity
- Metric: Index; averaged for S&P 500 constituents over the first half of 2018.
- Trading volumes serve as a proxy for liquidity.
- Liquidity indicator scale labels shown: 050100 (as displayed on the figure).
- Labeling of jump types on liquidity axis: Less liquid — Infinite activity.
- Sector list (with presentation order from less liquid to more liquid as in the figure): Telecom, Energy, Cons staples, Materials, Real estate, Utilities, Health care, Industrials, Cons disc, Financials, IT.
- Numeric markers alongside sectors shown in the figure: 18, 1.2, 1.4, 1.8, 2.2, 1.6, 2.0, 2.4, 0.8, 1.0, 2.6, 1.3, 1.8, 1.4, 1.5, 1.6, 1.7 (these appear in the graphic layout).

### Data and methods notes
- Sources: Bloomberg Finance L.P.; and IMF staff estimates.
- Method notes:
  - In panel 1, “Method 1” is based on Huang and Tauchen (2005) and Andersen and others (2006); “Method 2” is based on Ait-Sahalia and Jacod (2012).
  - In panel 2, frequency of jumps is based on the former method, and variation explained by jumps is based on the latter method.
  - In panel 3, the black and gray lines measure the type of jump: finite activity versus infinite activity; circle size depicts VIX on selected stress event days.
  - In panel 4, trading volumes serve as a proxy for liquidity.
- Abbreviations as used in the figure:
  - Cons = consumer
  - cons disc = consumer discretionary
  - IT = information technology
  - telecom = telecommunications

*Sources: Bloomberg Finance L.P.; and IMF staff estimates.*

---


_Source: https://www.imf.org/-/media/files/publications/gfsr/2018/oct/ch1/pdf/boxfigure1-4-1.pdf_