VIXCLS: CBOE Volatility Index Daily Closing Values from FRED (1990–2026)

The VIXCLS series covers daily implied volatility since January 1990 — over 8,800 observations spanning every major volatility episode from the 1990 Gulf War shock to the present. The index is calculated by Cboe and distributed through FRED under a Cboe copyright (citation required); Eco3min provides the chart and the analytical layer, and points to FRED as the distribution channel for the data itself.

Dataset: CBOE VIX Volatility Index (1990–present)

Source: Federal Reserve Bank of St. Louis (FRED). Chart generated and served by FRED.

Macro Takeaway

Dataset Overview

Licensing note: VIXCLS is the property of Cboe and is flagged “Copyrighted: Citation Required” on FRED. Eco3min therefore does not redistribute the file; the complete daily history since 1990 remains freely accessible from FRED via the direct link below.

Dataset Variables

The FRED CSV contains the following columns. Each row represents one business day.

Missing values (market holidays) appear as “.” in the FRED CSV.

FRED Direct CSV Access

The complete daily series since 1990 is available from FRED under series code VIXCLS:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=VIXCLS

This URL returns the full history in CSV format. It can be used directly in pandas, R, curl, or any data tool — no download or API key required.

Using the Dataset in Python

import pandas as pd

url = "https://fred.stlouisfed.org/graph/fredgraph.csv?id=VIXCLS"
df = pd.read_csv(url, parse_dates=["observation_date"], na_values=".")

print(f"Latest close: {df['VIXCLS'].dropna().iloc[-1]:.2f}")
print(f"Long-run average: {df['VIXCLS'].mean():.2f}")
print(f"Record close: {df['VIXCLS'].max():.2f} on {df.loc[df['VIXCLS'].idxmax(), 'observation_date'].date()}")

Using the Dataset in R

library(readr)

url <- "https://fred.stlouisfed.org/graph/fredgraph.csv?id=VIXCLS"
df <- read_csv(url, na = ".")

head(df)
summary(df$VIXCLS)

Both examples load the data directly from FRED — no download or API key required.

Methodology

VIXCLS is calculated by the Chicago Board Options Exchange (CBOE) using a model-free implied volatility formula applied to S&P 500 index options. The methodology aggregates the weighted prices of out-of-the-money puts and calls across a wide range of strike prices to estimate the market’s expectation of 30-day annualized variance. The step-by-step construction of this calculation is broken down in our explainer on how the VIXCLS index is computed from S&P 500 options.

The index is expressed in annualized percentage points. A VIXCLS reading of 20 implies an expected daily S&P 500 move of approximately ±1.26% over the coming month (20 ÷ √252). The index is calculated continuously during US equity trading hours, with the FRED VIXCLS series tracking the official daily closing value.

The current VIX methodology has been in place since September 2003. Prior values back to 1990 are reconstructed by CBOE using the same formula, with the earlier VXO methodology (based on at-the-money S&P 100 options) available as a separate series for users requiring the original definition.

The chart above is generated and served by FRED and always reflects the latest published close.

Data Quality & Provider Notes

VIXCLS is one of the most widely tracked financial series and has high data integrity. CBOE publishes the index in real time during US equity trading hours, with the official daily close finalized at 16:15 ET.

• Licensing (important). The series is the property of Cboe and carries a “Copyrighted: Citation Required” flag on FRED. Cite it as “Chicago Board Options Exchange, CBOE Volatility Index: VIX [VIXCLS], retrieved from FRED”. Redistribution of the file itself requires the owner’s permission, which is why Eco3min links to FRED rather than mirroring the data.
• Release latency. CBOE disseminates VIXCLS intraday; FRED publishes the official daily close on the next business morning (T+1, ~09:00 ET).
• Revisions policy. VIXCLS is not revised. Closing values are derived from final settlement prices of S&P 500 option strikes and finalized at end of trading day.
• Alternative sources. CBOE direct download (cboe.com/vix), Bloomberg ticker VIX, Refinitiv .VIX. Values are identical across providers; differences only arise in historical reconstructions prior to 1990 where some vendors splice the original VXO methodology rather than the post-2003 model-free formula.
• Known gaps. Weekends and US equity-market holidays (NYSE schedule). Brief gaps during exceptional closures: markets were closed September 11–16, 2001, with no VIXCLS observation for those dates.

For tick-level or intraday data, refer directly to CBOE.

Common Pitfalls When Using VIXCLS

VIXCLS is widely used as a single-number proxy for market stress, but several recurring interpretation errors distort the signal.

1. Confusing implied and realized volatility. Users often treat VIXCLS as a measure of how much the S&P 500 did move, when it actually measures how much option markets expect it to move over the next 30 days. The two diverge: realized volatility can spike days before implied volatility catches up, or remain elevated long after VIXCLS has decayed.
2. Ignoring the 30-day forward horizon. VIXCLS is an annualized expectation of 30-day variance — a reading of 20 implies roughly ±1.26% daily moves over the coming month, not the current day. Spot interpretation as “today’s volatility” is incorrect.
3. Reading absolute levels without regime context. A VIXCLS reading of 15 in 2017 (Fed QE era, suppressed term premia) and a reading of 15 in 2025 (post-pandemic structural regime) carry different signal weights. Percentile against a rolling 5-year window is more informative than the raw level.
4. Assuming a mechanical inverse with the S&P 500. While VIXCLS and S&P 500 returns are strongly negatively correlated on average (rolling correlation typically around –0.7), the relationship breaks during specific episodes — flat markets with rising VIXCLS during option re-pricing, or simultaneous rallies in both during melt-up phases driven by aggressive call-buying.

Historical Regimes

1990–1996 — Moderate volatility baseline. VIXCLS averaged approximately 17–19, with spikes during the 1990 Gulf War recession and the 1994 bond market rout. The yield curve inversions across these recessions provide complementary stress context. This period established the baseline behavior of the index.

1997–2003 — Elevated structural volatility. The Asian crisis (1997), LTCM collapse (1998), dot-com crash (2000–2002), and 9/11 attacks produced sustained VIXCLS readings above 25. The index peaked at 45 during the September 2001 episode.

2004–2007 — The great suppression. VIXCLS fell to historic lows around 10–12 as structured credit products and central bank liquidity suppressed both realized and implied volatility. In retrospect, this extreme complacency preceded the worst financial crisis since 1929 — a dynamic explored in our contrarian almanac study on VIX-driven equity returns since 1990.

2008–2009 — Record spike. VIXCLS reached an all-time closing high of 80.86 on November 20, 2008, as the global financial system approached the edge of systemic collapse. This reading implied expected daily S&P 500 moves of approximately ±5%.

2010–2019 — Fed-suppressed volatility. Quantitative easing programs compressed volatility structurally, with VIXCLS averaging near 15 and spending extended periods below 12. The “short volatility” trade became one of the most crowded positions in financial markets, culminating in the February 2018 “volmageddon” episode.

2020 — COVID shock. VIXCLS spiked to 82.69 intraday on March 16, 2020 — matching the GFC peak. The subsequent normalization was driven by aggressive monetary and fiscal response, with the NFCI easing back to neutral within months — a dynamic discussed in our stress-threshold audit comparing NFCI and VIX signals.

2022–present — Higher structural range. Post-pandemic, VIXCLS has settled into a structural range above the 2010s baseline, averaging closer to 17–18 versus 14–15 in the Fed-suppressed decade. Episodic spikes (2022 rate shock, 2023 banking stress) have been brief and well-bid, with mean-reversion reasserting within weeks.

Related Macroeconomic Datasets

VIXCLS measures one channel of market stress — equity option pricing. Triangulating with credit spreads, financial-conditions composites, and the underlying equity index sharpens the signal: divergences between VIXCLS and credit metrics often reveal whether stress is concentrated in positioning or in fundamentals.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Sources

• Chicago Board Options Exchange (CBOE) — VIX Index methodology and primary calculation
• Federal Reserve Bank of St. Louis — FRED series VIXCLS

Dataset Reference