VIX Daily Index

Rows: 9,193
Columns: 5

Daily CBOE Volatility Index (VIX) time-series data.

AI analysis

VIX Daily Index — Dataset Analysis

1. Dataset Overview & Research Value This dataset captures the CBOE Volatility Index (VIX) in daily OHLC (Open, High, Low, Close) format, sourced from DataHub.io's curated core finance collection (datahub.io/core/finance-vix), which aggregates and maintains standardized financial time-series data. With 9,193 daily records, the dataset likely spans roughly 36+ years of trading days (approximately 252 trading days per year), making it an exceptionally rich longitudinal resource for studying market fear and uncertainty cycles. The VIX is widely regarded as the market's "fear gauge," reflecting the implied volatility of S&P 500 index options — meaning it holds enormous potential for correlation studies against equity returns, economic indicators, credit spreads, and macro events. Its long temporal span makes it particularly suitable for regime-change analysis, crisis detection, and cross-asset behavioral studies.

2. Data Quality Observations The dataset presents excellent overall quality. All five columns report zero null cells across 9,193 rows, yielding a 100% completeness rate with no imputation concerns. No type mismatches are indicated — DATE is cleanly typed as a Date column and all price columns are properly cast as Decimals. Duplicate row figures are noted as pending Phase D recomputation, so that should be confirmed before finalizing any pipeline, but the fact that DATE has 9,193 distinct values matching exactly 9,193 total rows is a strong implicit indicator of zero date-level duplicates — each trading day appears to be uniquely represented. The only quality nuance worth flagging is the outlier counts: OPEN (312), HIGH (332), LOW (298), and CLOSE (323) all carry a meaningful number of statistical outliers, which are not errors but rather genuine market extremes (e.g., the 2008 financial crisis, March 2020 COVID spike) that must be handled carefully in any modeling context to avoid skewing regression or correlation coefficients.

3. Key Column Distributions The CLOSE column is typically the canonical VIX reference and its statistics tell a compelling story: a mean of 19.45 versus a median of 17.61 immediately signals right-skewed distribution — confirmed by a skewness of 2.21 — meaning the VIX spends most of its time in a lower, calmer range but is periodically dragged upward by sharp volatility spikes. The interquartile range (Q1=13.96 → Q3=22.73) shows that 50% of all trading days fall within a relatively narrow 9-point band, reinforcing the idea that extreme readings are infrequent but dramatic. HIGH tells the most extreme story with a max of 89.53 and skewness of 2.35, the highest across all columns, reflecting intraday panic spikes that briefly breach the daily range. The spread between LOW (mean=18.77) and HIGH (mean=20.38) — approximately 1.6 index points on average — gives a rough baseline for typical daily intraday volatility-of-volatility. The OPEN column's minimum of 2.58 deserves a data provenance check, as it sits notably below the historical record and may reflect early index construction artifacts or a data anomaly.

4. Recommended Join Key Columns DATE is the natural and only join key here, and it is ideally suited for the role — 100% populated, fully distinct, and typed correctly as a Date. For cross-dataset joins, it should be normalized to a standard YYYY-MM-DD format and aligned to trading calendar days only, since VIX is not published on weekends or U.S. market holidays. When joining to datasets with different temporal granularities (e.g., monthly economic releases or weekly survey data), CLOSE on the last trading day of the period is the standard aggregation convention. A year-month derived key (DATE truncated to month) would also be valuable for joining macro datasets at monthly resolution without losing intraday or daily structure in the VIX data itself.

5. Suggested Pairing Datasets for Correlation Discovery Several dataset categories would pair powerfully with this data. S&P 500 daily price data is the most natural companion — the VIX is derived from S&P 500 options, and the well-documented negative correlation between VIX and SPX returns (~−0.7 to −0.8) could be quantified and its regime-specific breakdowns explored. U.S. Treasury yield data (particularly the 10-year and 2-year yields) would allow investigation into whether credit stress and equity volatility co-move during risk-off episodes. Federal Reserve economic data (FRED) — including unemployment claims, CPI, and GDP growth — could reveal how macroeconomic deterioration leads or lags volatility spikes. Corporate credit spread datasets (e.g., ICE BofA High Yield Index) are theoretically coupled with VIX as dual measures of risk appetite. Finally, Google Trends or news sentiment data around terms like "recession" or "market crash" could provide an unconventional but potentially high-signal behavioral correlation layer, especially around the dataset's identifiable spike events in 2008, 2011, 2018, and 2020.

Columns

DATE (date)
OPEN (decimal)
HIGH (decimal)
LOW (decimal)
CLOSE (decimal)