Detecting VIX Term Structure Regimes
Spot Contango, Backwardation, and regimes with PCA and HMM by analyzingn the VIX Term Structure in Python
Investors watch the VIX tick higher and assume they see fear. But few understand what it really means.
A single VIX number tells you almost nothing about where risk sits in the market. On the other hand, the term structure tells you everything.
For instance, ff the entire curve shifts up, traders price in uncertainty everywhere. If the front spikes and the back stays flat, the market expects a short shock.
We focus on the VIX term structure because it answers questions the index alone cannot:
- Does the crowd expect a crisis next week or three months from now?
- Is the market bracing for a known event, or does fear stretch across the entire curve?
- When does panic actually show up and when does it fade?
The complete Python notebook for the analysis is provided below.
In this article, we’ll cover the following:
- How to extract real-time VIX futures term structure data
- What the level, slope, and curvature of the VIX curve show
- How to visualize curve shifts and spot market regime changes
- Interpretation of contango, backwardation, and event risk patterns
- How to measure and track VIX carry spreads (short-term vs long-term)
- Breaking down term structure moves into level, slope, and curvature
- Using a constant-maturity 30-day VIX futures index as a volatility signal
1. Why VIX Term Structure Matters
CBOE formally defines the VIX as the square root of a weighted blend of option prices that replicate a 30-day variance swap on the S&P 500.
Essentially, VIX reflects the market’s consensus on how much volatility lies ahead for the next 30 days.
Every news outlet calls it the “fear index”. That’s not wrong, but it’s incomplete.
What the headline VIX hides:
- It only covers one maturity (30 days).
- It ignores how fear moves across time.
- It misses where risk concentrates, i.e. right now, or months from now.
What Is the VIX Term Structure?
The VIX term structure tracks the price of volatility for every future month. One curve for each trade date, built from VIX futures.
- Each point: “What does it cost to hedge against market swings for this expiry?”
- The curve: “Where do traders expect risk to cluster, e.g. immediately, or later?”
With this, you get to see not only how nervous the market is, but also when.
There are two main VIX term structure regimes:
- Contango: The curve slopes upward. Short-term futures trade below long-term. The market expects volatility to fade over time. This is the normal state.
- Backwardation: The curve slopes downward. Short-term futures trade above long-term. The market expects immediate risk or panic. This is rare and signals stress.
These regimes are illustrated in the animation below. We’ll discuss it in greater detail.
Figure 1. The Shape of difference of a Contago and Backwardation Regime in the VIX Term Structure. Plot created by author.
How to Read the VIX Curve: Level, Slope, Curvature
Level:
Level measures the average height of the VIX term structure. If the whole curve rises, every expiry costs more.
Markets expect high volatility across all horizons. Sustained high levels often signal persistent macro stress.
When level jumps, every risk model needs to adjust. Option sellers step back. Hedgers pay up. ETF roll costs spike.
Figure 2. VIX Term Structure Level Shift Illustration. The entire curve moves up and down together. Shows market repricing overall volatility. Plot created by author.
Slope:
Slope shows the difference between short-term and long-term volatility.
A positive slope means contango: long-term futures trade above the front. The market expects fear to fade.
Most of the time, VIX futures sit in contango. This structure creates carry: short-term volatility recovers faster than long-term volatility.
A negative slope means backwardation: front contracts trade above the back. Panic is here. Markets demand immediate protection.
This only happens in true stress events, e.g. crashes, liquidity shocks, major policy surprises.
Figure 3. VIX Term Structure Slope Shift Illustration. Back end pivots while the front stays fixed. Displays steepening and flattening only. Plot created by author.
Curvature:
Curvature captures how the middle of the curve sits relative to the ends.
A positive curvature forms a hump: the market sees more risk in the mid-term than either end.
Traders brace for a specific event (elections, debt ceiling, major data releases) in a few months.
A negative curvature, i.e. a dip, signals the market expects calm now and in the distant future, but a shock in the middle.
Event risk often appears here before headlines pick it up.
Figure 4. VIX Term Curvature Slope Shift Illustration. Mid maturities bulge or dip versus the wings. Isolates belly‑focused demand changes. Plot created by author.
Carry Forward Risk:
Carry forward risk appears when the front of the curve recovers faster than the back.
This creates profitable carry trades in contango, i.e. long-dated protection decays, short-term contracts settle lower.
In backwardation, carry flips: front contracts bleed premium, and volatility ETF products see sharp gains or losses.
Watch for carry spread signals; they tell you when the short end expects quick normalization while the long end still prices in uncertainty.
Figure 5. Carry Foward Risk. VX2‑VX1 decays faster than VX6‑VX1. Positive carry when the front spread narrows first; carry flips when VX2‑VX1 turns negative while VX6‑VX1 stays elevated. Plot created by author.
2. Get the VIX Term Structure Data from CBOE
We use vix_utils, a Python wrapper that pulls VIX futures term-structure data directly from CBOE’s public CSV endpoints. Under the hood, it:
- Opens an HTTP connection to CBOE’s daily term-structure CSV URLs
- Uses aiohttp and asyncio to request all maturities in parallel
- Calls pandas.read_csv on the raw CSV response to build a DataFrame
!pip install -q vix_utils hmmlearn
Key fields in the returned df:
- Trade Date: Snapshot date for each quote
- Expiry: Futures expiration date
- Tenor_Days: Days until expiry
- Tenor_Monthly: Rounded month count
- Settle: Implied volatility level (VIX points)
- Weekly / Expired: Flags to exclude illiquid contracts
import warnings
# Suppress FutureWarnings from vix_utils
warnings.filterwarnings(
"ignore",
category=FutureWarning,
module="vix_utils"
)
import nest_asyncio
import asyncio
from vix_utils import async_load_vix_term_structure
# Patch the event loop for nested use
nest_asyncio.apply()
async def get_term_structure():
"""
Load the VIX term structure asynchronously.
Returns a pandas DataFrame.
"""
df = await async_load_vix_term_structure()
return df
# Run the async loader and display the first rows
loop = asyncio.get_event_loop()
df = loop.run_until_complete(get_term_structure())
Figure 6. Snapshot of the raw VIX futures term structure data, including trade date, expiry, tenor, and settlement prices across contracts.
Later we’ll use HMMlearn (installed above) in the next section to fit a Gaussian Hidden Markov Model on slope series and classify regimes.
HMMlearn follows the classic scikit-learn conventions and uses the EM algorithm for parameter estimation.
3. Visualize the VIX Term Structure Over Time
We use interactive visualizations to track how the VIX curve shifts, flattens, and inverts over time.
The slider chart shows each day’s term structure.
- Each line is a snapshot: expiry on the x-axis, VIX futures price on the y-axis.
- The regime label updates with each date, i.e. Contango, Backwardation, or Cautious, based on the slope between front and back contracts.
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