fredgraph.csv?id=VIXCLS — Direct CSV Download (VIX FRED)
The CBOE Volatility Index (VIX) measures the market’s expectation of 30-day forward volatility, derived from S&P 500 index options prices. Often called the “fear gauge,” the VIX rises when investors are willing to pay more for downside protection. This dataset provides daily closing values from the FRED series VIXCLS since 1990.
Dataset: CBOE VIX Volatility Index (1990–2026) · Updated —
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Source: FRED series VIXCLS · Federal Reserve Bank of St. Louis
Macro Takeaway
The VIX is structurally mean-reverting — it spikes during stress events and then decays back toward a long-run average of approximately 19-20. This mean-reversion property makes absolute VIX levels informative: readings below 13 historically coincide with periods of complacency and compressed risk premia, while readings above 30 indicate acute fear and typically precede the best forward equity returns (buying fear has been systematically rewarded).
The VIX also has a structural relationship with credit spreads: both measure risk appetite, but through different channels. When the VIX and HY spreads diverge — for example, VIX rising while credit spreads remain tight — it often signals a correction risk concentrated in equity positioning rather than a fundamental deterioration. When both spike simultaneously, the signal is more ominous.
Dataset Overview
| Indicator | CBOE VIX Volatility Index (1990–2026) |
|---|---|
| Geography | United States |
| Frequency | Daily (business days) |
| Period | 1990–2026 |
| Variables | date, vix_close |
| Format | CSV, Excel (XLSX) |
| Sources | Federal Reserve Bank of St. Louis — FRED |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
vix_close | Float | vix_close value |
Column names match the CSV headers exactly.
Download the Complete Dataset
The full dataset is available in CSV and Excel formats.
FRED Direct CSV Access
The underlying data is available from FRED under series code VIXCLS:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=VIXCLS
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/vix-index.csv
This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.
Using the Dataset in Python
import pandas as pd url = "https://eco3min.fr/dataset/vix-index.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["vixcls"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/vix-index.csv" df <- read_csv(url) head(df) summary(df$vixcls)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
The VIX is calculated by the Chicago Board Options Exchange (CBOE) using a model-free implied volatility formula applied to S&P 500 index options. It 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 volatility.
The VIX is expressed in percentage points of annualized volatility. A VIX of 20 implies the market expects the S&P 500 to move approximately ±1.2% per day (20% / √252). The index is calculated in real-time during trading hours.
This dataset is updated weekly (Saturday 08:00 UTC) via automated pull from the FRED API.
Historical Regimes
1990–1996 — Moderate volatility. VIX averaged approximately 17-19, with spikes during the 1990 Gulf War recession and the 1994 bond market rout. 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 VIX readings above 25. The index peaked at 45 during the September 2001 crisis.
2004–2007 — The great suppression. VIX fell to historic lows around 10-12 as structured credit products and central bank liquidity suppressed realized and implied volatility. In retrospect, this extreme complacency was the prelude to the worst financial crisis since 1929.
2008–2009 — Record spike. The VIX reached an all-time closing high of 80.86 on November 20, 2008, as the global financial system teetered on collapse. This reading implied expected daily S&P 500 moves of ±5%.
2010–2019 — Fed-suppressed volatility. QE programs compressed volatility structurally, with the VIX averaging around 15 and spending extended periods below 12. The “short volatility” trade became one of the most crowded strategies in financial markets.
2020–present — COVID and normalization. The VIX spiked to 82.69 intraday on March 16, 2020 — matching the GFC peak. The subsequent normalization has been punctuated by episodic spikes (2022 rate shock, 2023 banking stress), with the VIX settling into a higher structural range than the pre-COVID era.
Related Macroeconomic Datasets
The VIX measures equity market implied volatility — but it is not the only stress indicator. Credit spreads measure default risk, financial conditions indices capture systemic stress, and liquidity measures reveal the plumbing dynamics that can amplify or dampen volatility. When VIX and credit spreads diverge, the signal is about positioning, not fundamentals.
- US High Yield Credit Spread — Credit stress: when both VIX and HY spreads spike, the signal is systemic
- Financial Conditions Index (NFCI) — 105-indicator composite vs VIX’s single-channel equity focus
- S&P 500 Price Index — The underlying equity benchmark priced by VIX options
- Net Liquidity Index — Liquidity conditions that influence volatility regimes
- US 10-Year Treasury Yield — Rate environment shaping risk appetite
Macroeconomic Dataset Hub
This dataset is part of the Eco3min macro-financial data repository.
Explore the Eco3min Dataset Hub
Sources
- Chicago Board Options Exchange (CBOE) — VIX Index
- Federal Reserve Bank of St. Louis — FRED series VIXCLS