Credit Spread vs VIX — Daily CSV Download (Risk Divergence Indicator)
The relationship between high yield credit spreads and equity volatility (VIX) reveals whether risk is being priced consistently across asset classes. When credit spreads widen while VIX remains low — or vice versa — it signals a divergence that has historically resolved with a repricing in one or both markets. This composite dataset tracks both series for cross-asset risk analysis.
Dataset: Credit Spread vs VIX Divergence (1997–2026) · Updated —
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Source: FRED series BAMLH0A0HYM2 · ICE BofA (via FRED) & CBOE (via FRED)
Macro Takeaway
This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.
Cross-referencing with the high yield credit spreads and the VIX volatility index helps situate this indicator within the broader macro regime.
Dataset Overview
| Indicator | Credit Spread vs VIX Divergence (1997–2026) |
|---|---|
| Geography | United States |
| Frequency | Weekly |
| Period | 1997–2026 |
| Variables | date, hy_oas_bps, vix_close |
| Format | CSV, Excel (XLSX) |
| Sources | ICE BofA (via FRED) & CBOE (via FRED) |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
hy_oas_bps | Float | ICE BofA HY OAS in basis points |
vix_close | Float | VIX index closing 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 BAMLH0A0HYM2:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=BAMLH0A0HYM2
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/credit-spread-vs-vix.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/credit-spread-vs-vix.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["hy_oas_bps"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/credit-spread-vs-vix.csv" df <- read_csv(url) head(df) summary(df$hy_oas_bps)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
High Yield OAS from ICE BofA (FRED: BAMLH0A0HYM2) and VIX from CBOE (FRED: VIXCLS), aligned on weekly frequency. Both series are option-adjusted or implied — they represent market-priced risk, not realized outcomes.
This dataset is updated weekly via automated pull from the FRED API.
Historical Regimes
Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.
Related Macroeconomic Datasets
Credit and equity volatility price different aspects of corporate risk. HY spreads reflect default probability over the maturity of the bond; VIX reflects 30-day implied equity volatility. Divergences between the two often precede regime shifts.
- US High Yield Credit Spread — The credit leg of the divergence pair
- VIX Volatility Index — The equity volatility leg of the divergence pair
- S&P 500 Price Index — Equity benchmark that VIX is derived from
- Investment Grade BBB Spread — IG spread for comparison
- Financial Conditions Index (NFCI) — Composite that captures both credit and equity risk
Related Research
When credit breaks first (spreads widen before equities sell off), the credit-VIX divergence provides the earliest warning. This dataset enables systematic detection of those misalignments.
Macroeconomic Dataset Hub
This dataset is part of the Eco3min macro-financial data repository.
Explore the Eco3min Dataset Hub
Sources
- ICE BofA (via FRED) & CBOE (via FRED)