Market volatility statistics: what the VIX history says about calm, stress, and shock
Market volatility tends to look abstract until you line up the numbers. The Cboe VIX history shows a long stretch of mid-teens averages interrupted by violent spikes, and those spikes are the clearest reminder that markets can shift from placid to unstable very quickly (Cboe VIX Historical Data).
This page walks through the full pattern in the provided statistics: annual averages from 1990 through 2024 and the biggest daily closes on record in the file. The data makes one thing obvious: volatility is usually moderate, but the tail events are extreme.
Table of contents
- Fast facts
- Annual VIX averages by year
- What the big spikes show
- How the calm years differ
- Reading the extremes without overreading them
- Useful ways to use these statistics
Fast facts
- The highest annual average VIX close in the supplied list is 32.69 in 2008 (Cboe VIX Historical Data).
- The next highest annual average is 31.48 in 2009 (Cboe VIX Historical Data).
- The lowest annual average in the supplied list is 11.09 in 2017 (Cboe VIX Historical Data).
- The highest daily close in the file is 82.69 on 2020-03-16 (Cboe VIX Historical Data).
- The next highest daily closes are 80.86 on 2008-11-20 and 80.06 on 2008-10-27 (Cboe VIX Historical Data).
- The supplied annual averages show a long run of values between roughly 12 and 18 outside of crisis years (Cboe VIX Historical Data).
The data does not describe every cause of volatility. It does show when volatility was unusually elevated and when it returned to calmer levels.
Annual VIX averages by year
The annual averages in the supplied statistics create a clean timeline from 1990 through 2024. Looking at them together is more useful than reading them one year at a time because the pattern is shaped by clusters, not isolated points (Cboe VIX Historical Data).
| Year | Annual average VIX close | Source |
|---|---|---|
| 1990 | 23.06 | Cboe VIX Historical Data |
| 1991 | 18.37 | Cboe VIX Historical Data |
| 1992 | 15.45 | Cboe VIX Historical Data |
| 1993 | 12.69 | Cboe VIX Historical Data |
| 1994 | 13.93 | Cboe VIX Historical Data |
| 1995 | 12.39 | Cboe VIX Historical Data |
| 1996 | 16.44 | Cboe VIX Historical Data |
| 1997 | 22.36 | Cboe VIX Historical Data |
| 1998 | 25.60 | Cboe VIX Historical Data |
| 1999 | 24.37 | Cboe VIX Historical Data |
| 2000 | 23.32 | Cboe VIX Historical Data |
| 2001 | 25.75 | Cboe VIX Historical Data |
| 2002 | 27.29 | Cboe VIX Historical Data |
| 2003 | 21.98 | Cboe VIX Historical Data |
| 2004 | 15.48 | Cboe VIX Historical Data |
| 2005 | 12.81 | Cboe VIX Historical Data |
| 2006 | 12.81 | Cboe VIX Historical Data |
| 2007 | 17.54 | Cboe VIX Historical Data |
| 2008 | 32.69 | Cboe VIX Historical Data |
| 2009 | 31.48 | Cboe VIX Historical Data |
| 2010 | 22.55 | Cboe VIX Historical Data |
| 2011 | 24.20 | Cboe VIX Historical Data |
| 2012 | 17.80 | Cboe VIX Historical Data |
| 2013 | 14.23 | Cboe VIX Historical Data |
| 2014 | 14.18 | Cboe VIX Historical Data |
| 2015 | 16.67 | Cboe VIX Historical Data |
| 2016 | 15.83 | Cboe VIX Historical Data |
| 2017 | 11.09 | Cboe VIX Historical Data |
| 2018 | 16.64 | Cboe VIX Historical Data |
| 2019 | 15.39 | Cboe VIX Historical Data |
| 2020 | 29.25 | Cboe VIX Historical Data |
| 2021 | 19.66 | Cboe VIX Historical Data |
| 2022 | 25.64 | Cboe VIX Historical Data |
| 2023 | 16.85 | Cboe VIX Historical Data |
| 2024 | 15.55 | Cboe VIX Historical Data |
Several broad periods stand out.
Early 1990s: elevated start, then easing
The series begins at 23.06 in 1990 and 18.37 in 1991 before moving lower into the mid-teens and eventually down to 12.39 in 1995 (Cboe VIX Historical Data). That sequence suggests a shift from an initially unsettled environment toward a calmer one, at least by the measure used in this file.
The move from 23.06 in 1990 to 12.39 in 1995 is not a small change. It is the difference between a market that is consistently tense and one that is spending much more time in a lower-volatility regime (Cboe VIX Historical Data).
Late 1990s and early 2000s: a second surge
The next sustained rise appears in 1997 through 2002. Annual averages climb from 22.36 in 1997 to 25.60 in 1998, then stay high at 24.37 in 1999 and 23.32 in 2000 before moving to 25.75 in 2001 and peaking in the annual averages at 27.29 in 2002 (Cboe VIX Historical Data).
That five-year stretch is notable because the values do not just spike once. They remain persistently elevated. For readers trying to understand market volatility statistics, persistence matters as much as peak height. A single high reading may be a shock; a multi-year run above 20 points to a different kind of market environment.
Mid-2000s calm, then the crisis jump
After 2002, the annual average falls to 21.98 in 2003 and then down to 15.48 in 2004, followed by 12.81 in both 2005 and 2006 (Cboe VIX Historical Data). The mid-2000s look much calmer than the surrounding periods.
Then 2007 rises to 17.54, and 2008 jumps to 32.69. That is the highest annual average in the dataset and a clear break from the mid-2000s calm (Cboe VIX Historical Data).
Post-crisis normalization, then another shock
The years after the crisis do not stay at crisis levels. The annual average drops to 31.48 in 2009, then to 22.55 in 2010, 24.20 in 2011, and eventually back into the high teens and mid-teens in later years such as 2012 through 2019, including 11.09 in 2017, the lowest annual average in the file (Cboe VIX Historical Data).
Then 2020 jumps to 29.25, followed by 19.66 in 2021 and 25.64 in 2022, before easing again to 16.85 in 2023 and 15.55 in 2024 (Cboe VIX Historical Data). The shape is familiar: calm, then a shock, then a gradual return toward lower levels.
What the big spikes show
The largest daily closes in the file are much more dramatic than the annual averages, which is exactly what you would expect from a volatility measure. Daily stress can be severe even when the annual average is not the highest on record.
| Rank | Daily VIX close | Date | Source |
|---|---|---|---|
| 1 | 82.69 | 2020-03-16 | Cboe VIX Historical Data |
| 2 | 80.86 | 2008-11-20 | Cboe VIX Historical Data |
| 3 | 80.06 | 2008-10-27 | Cboe VIX Historical Data |
| 4 | 79.13 | 2008-10-24 | Cboe VIX Historical Data |
| 5 | 76.45 | 2020-03-18 | Cboe VIX Historical Data |
| 6 | 75.91 | 2020-03-17 | Cboe VIX Historical Data |
| 7 | 75.47 | 2020-03-12 | Cboe VIX Historical Data |
| 8 | 74.26 | 2008-11-19 | Cboe VIX Historical Data |
| 9 | 72.67 | 2008-11-21 | Cboe VIX Historical Data |
| 10 | 72.00 | 2020-03-19 | Cboe VIX Historical Data |
| 11 | 70.33 | 2008-10-17 | Cboe VIX Historical Data |
| 12 | 69.96 | 2008-10-29 | Cboe VIX Historical Data |
| 13 | 69.95 | 2008-10-10 | Cboe VIX Historical Data |
| 14 | 69.65 | 2008-10-22 | Cboe VIX Historical Data |
| 15 | 69.25 | 2008-10-15 | Cboe VIX Historical Data |
The concentration of extremes in two periods
The top 15 daily closes cluster around 2008 and 2020. That concentration matters because it shows the file is not describing a steady stream of very high readings across every year. Instead, it highlights two major volatility episodes that dominate the tail of the distribution (Cboe VIX Historical Data).
For market analysis, that is a useful distinction. Most of the time, volatility lives in a moderate range. The extreme tail is where risk management becomes most important, and this dataset places those extremes in a small number of crisis windows.
2008 and 2020 are different, but both severe
The top five daily closes span both 2008 and 2020, with the highest being 82.69 on 2020-03-16 and the next three all occurring in late 2008 (Cboe VIX Historical Data). The file therefore suggests that 2020 and 2008 both produced exceptional stress, with 2020 narrowly taking the top spot on the single-day list.
That said, the annual averages tell a different story. 2008 posts 32.69, while 2020 posts 29.25. So the 2020 shock produced the single highest daily close, but 2008 produced the higher average across the full year (Cboe VIX Historical Data).
How the calm years differ
The calmest years in the list are not identical, but they share a low-to-mid-teens profile.
- 2017 is the lowest annual average at 11.09 (Cboe VIX Historical Data).
- 2005 and 2006 both sit at 12.81 (Cboe VIX Historical Data).
- 1993 is 12.69 and 1995 is 12.39 (Cboe VIX Historical Data).
- 2014 is 14.18 and 2013 is 14.23 (Cboe VIX Historical Data).
- 2024 is 15.55, which is not a record low but still firmly within the calmer range (Cboe VIX Historical Data).
The important point is that calm does not mean zero movement. Even the lowest annual average in the file, 11.09, is still a meaningful level of market variability. The data simply shows that some years spend much more time in a restrained regime than others.
A compact comparison of selected years
| Year | Annual average VIX close | Interpretation from the sequence |
|---|---|---|
| 2002 | 27.29 | High, persistent stress period |
| 2006 | 12.81 | Very calm by the dataset’s standards |
| 2008 | 32.69 | Highest annual average in the file |
| 2017 | 11.09 | Lowest annual average in the file |
| 2020 | 29.25 | Another major stress year |
| 2024 | 15.55 | Back near the calmer mid-teens range |
This comparison helps because it places the extremes and the quiet years on the same scale. The distance between 11.09 and 32.69 is large enough to remind you that volatility regimes are not subtle once they change (Cboe VIX Historical Data).
Reading the extremes without overreading them
These statistics are strong evidence of regime shifts, but they do not explain causation on their own. The file gives values, dates, and source labels. It does not provide a narrative for why each spike happened (Cboe VIX Historical Data).
That restraint is useful. It keeps the interpretation grounded.
What the numbers support
- Volatility is usually moderate, not extreme, based on the many annual averages in the low-to-mid teens (Cboe VIX Historical Data).
- Crisis periods can lift the annual average sharply for more than one year at a time, as seen in 2008, 2009, 2020, and 2022 (Cboe VIX Historical Data).
- Daily tail risk can be far more severe than annual averages suggest, with several readings above 75 in 2008 and 2020 (Cboe VIX Historical Data).
- The market can return to a quieter regime after a stress event, but the return is not immediate or perfectly linear, as shown by the post-2009 and post-2020 declines (Cboe VIX Historical Data).
What the numbers do not support by themselves
- They do not prove any single policy decision or event caused a volatility spike.
- They do not identify whether one crisis was “worse” in every sense, only that the measured VIX behavior differed by year and by day.
- They do not forecast future volatility. A historical series shows what happened, not what must happen next.
Useful ways to use these statistics
If you are using market volatility statistics for research, reporting, or content planning, this file is most useful when you treat it as a regime map.
1. Use annual averages to describe the backdrop
The annual average close is the cleanest way to summarize a year. It lets you say whether the market spent most of the year relatively calm, moderately tense, or highly stressed. In this dataset, 2008, 2009, 2020, and 2022 stand out as elevated years, while 2017 stands out as a particularly calm one (Cboe VIX Historical Data).
2. Use daily extremes to illustrate crisis behavior
Daily closes above 70 are rare enough to be memorable. In the supplied statistics, they concentrate in late 2008 and March 2020, which makes them useful for describing peak stress windows rather than ordinary trading conditions (Cboe VIX Historical Data).
3. Compare adjacent years, not just endpoints
The sequence matters. A jump from 17.54 in 2007 to 32.69 in 2008 is more informative than simply saying “2008 was high.” Likewise, the easing from 31.48 in 2009 to 22.55 in 2010 shows how volatility can decline after a crisis while remaining above very calm levels for a while (Cboe VIX Historical Data).
4. Separate yearly regime from one-day panic
The annual average and the daily peak tell different stories. For example, 2020 contains the highest daily close in the file, but 2008 has the higher annual average. That difference is valuable because it separates concentrated shock from sustained stress (Cboe VIX Historical Data).
5. Keep source labels visible in your own writeups
When you cite these figures, keep the source label close to the numbers. The source labeling in the dataset makes it easier to track the underlying series and avoids treating derived summaries as if they were independent observations (Cboe VIX Historical Data).
The simplest takeaway from the full set is that market volatility spends most of its time in a manageable range, then occasionally erupts into major stress episodes that dominate the record. The annual averages show the backdrop, and the daily closes show the panic peaks. Together, they give a compact but powerful picture of how volatility behaves over time (Cboe VIX Historical Data).