VIX Volatility Index Daily (FRED)

Rows: 9,496
Columns: 2

Daily CBOE Volatility Index (VIX) closing values from 1990 to present. The 'fear gauge' of the US stock market measuring implied volatility of S&P 500 options. Essential for financial risk research.

AI analysis

VIX Volatility Index Daily – Dataset Analysis

1. Dataset Overview & Research Value This dataset captures daily closing values of the CBOE Volatility Index (VIX) from 1990 to the present, sourced directly from the Federal Reserve Bank of St. Louis (FRED) via their public API at fred.stlouisfed.org. FRED is one of the most authoritative and consistently maintained economic data repositories, and the live API endpoint (fredgraph.csv?id=VIXCLS) suggests the dataset can be refreshed to near-current values, making it highly relevant for both historical backtesting and contemporary risk analysis. Often called the market's "fear gauge," the VIX measures the implied volatility priced into S&P 500 options over the next 30 days — making it a uniquely forward-looking sentiment indicator rather than a purely retrospective one. Its 9,496 daily observations spanning roughly 35 years represent nearly every US trading day across multiple full market cycles, giving it exceptional depth for longitudinal correlation studies.

2. Data Quality Observations The dataset is structurally clean, consisting of just two columns with no duplicate rows noted and zero nulls on the Date column. The primary quality concern is 302 null values in VIXCLS (3.2% of rows), which is a non-trivial gap for a daily financial time series. These missing values almost certainly correspond to non-trading days — weekends, federal holidays, and market closures — that appear as date records without a corresponding closing value. While expected in raw financial data, downstream analysis should handle these carefully: imputation (forward-fill or interpolation) may be appropriate for some use cases, but dropping nulls is safer for return-calculation or volatility-modeling workflows. The 2,528 distinct values across 9,194 non-null decimal readings indicates reasonable precision and no obvious rounding artifacts.

3. Key Column Distributions The VIXCLS column tells a rich statistical story. The mean of 19.45 sits well above the median of 17.61, immediately signaling right-skewed data — confirmed by a skewness coefficient of 2.21, which is substantial. This means extreme spikes (crisis events) pull the average up significantly from the typical "calm market" baseline. The interquartile range of Q1=13.96 to Q3=22.73 reflects that the VIX spends most of its time in a relatively contained band during normal conditions. However, the maximum of 82.69 (almost certainly the March 2020 COVID shock or October 2008 financial crisis) and 323 flagged outliers underscore how fat-tailed this distribution is — those outliers are not data errors, they are the signal. The minimum of 9.14 captures historically complacent market conditions (likely late 2017). Any correlation analysis must account for this non-normality; log-transforming VIX values before regression is strongly advisable.

4. Recommended Join Key The Date column is the unambiguous join key, with 9,496 fully distinct, non-null values — a perfect natural key for time-series alignment. When joining to other financial datasets, analysts should be aware of the trading-day calendar issue: this dataset will only have values on US market trading days, so joins to daily economic releases, international indices, or calendar-day datasets will require a date-spine or fill strategy to avoid row-count mismatches. ISO 8601 date formatting should be confirmed before joining to ensure compatibility across data sources.

5. Suggested Correlation Datasets Several dataset categories would pair powerfully with VIX data. S&P 500 daily returns (e.g., FRED series SP500 or Yahoo Finance) is the most natural companion — the well-documented inverse VIX-to-equity relationship can be quantified, and divergences from that relationship are themselves analytically valuable. Federal Funds Rate or Treasury yield data (also available via FRED) would enable study of how monetary policy environments modulate volatility regimes. Credit spread indices (e.g., ICE BofA High Yield OAS, FRED series BAMLH0A0HYM2) would reveal how equity fear and credit stress co-move during crises. For macro context, unemployment claims or GDP growth data could test whether realized economic deterioration leads or lags implied volatility spikes. Finally, international volatility indices (VSTOXX for Europe, VXJ for Japan) would support cross-market contagion analysis across the full 35-year crisis history this dataset captures.

Columns

Date (date)
VIXCLS (decimal)