CBOE Volatility Index
The VIX measures the market's expectation of 30-day forward-looking volatility, derived from S&P 500 index options. Often called the 'fear gauge,' it rises during periods of market uncertainty.
The seasonal data for VIX presents a striking divergence depending on which lens you apply. The last 10 consecutive years paint a deeply bearish picture, with 90-day win rates collapsing to just 10% and average returns of negative 23.66%. But midterm election years tell a completely different story: 60-day win rates jump to 62.5% with an average return of positive 7.76%, projecting a target near $36.
Midterm years historically generate volatility spikes as political uncertainty peaks ahead of November elections, a dynamic that overrides the typical seasonal suppression seen in the broader dataset. With VIX already down 8.27% recently, the market is pricing in calm, making a midterm-driven volatility surge the key contrarian setup to watch.
Seasonal Price Projections
Select a historical basis and projection horizon to see where seasonal patterns suggest CBOE Volatility Index may be headed.
Projection as of Mar 10, 2026 from closing price $23.39
How to Use This Data
Seasonal projection data for the CBOE Volatility Index reflects how the index has historically performed during this same calendar period across past years. The consecutive pattern shows a 20% win rate, meaning the index was higher at the 60-day mark in only 20% of those years. The midterm election year pattern tells a notably different story, with a 62.5% win rate and an average return of positive 7.8%.
When the two bases point in opposite directions, it signals that the election cycle may be creating conditions that diverge from recent consecutive history. The median return of positive 4.8% in midterm years is worth noting alongside the average, as the median is less distorted by outlier years like the historical extremes of positive 33.3% and negative 41.7%.
Seasonal patterns cannot account for sudden geopolitical developments, policy shifts, or macroeconomic surprises that fall outside historical norms. A 62% win rate still means the index declined in roughly 38% of comparable years, and no statistical tendency guarantees a specific outcome in any single year.
Market participants often use seasonal data as one layer of context alongside technical analysis, fundamental research, and broader risk frameworks. It can help calibrate expectations about historical tendencies during a given period without serving as a standalone basis for any decision.
This information is provided for educational purposes only and does not constitute financial advice, a recommendation, or a solicitation to buy or sell any security. Seasonal patterns are based on historical data and do not guarantee future performance. All investment decisions carry risk. Consult a qualified financial advisor before making investment decisions.
Understanding Seasonal Projections
Seasonal projections estimate future price movement based on how CBOE Volatility Index has historically performed during the same calendar period. These are statistical baselines derived from decades of market data, not predictions.
Consecutive Years (Last 10)
Uses the most recent 10 years of data regardless of market regime. This captures the broadest recent behavior, including all economic and political environments. Over the next 60 trading days, this pattern has been positive 2 of 10 times with an average return of -12.2%.
Midterm Election Years (8 Available)
Uses only years that fall in the same position within the 4-year U.S. presidential election cycle. 2026 is a midterm election year. Markets often exhibit distinct patterns tied to fiscal and monetary policy shifts within this cycle. In 8 historical midterm election years, this 60-day window was positive 5 times with an average return of +7.8%.
Seasonal patterns reflect historical tendencies and do not guarantee future results. All projections are based on past performance and should be used as one input among many in your investment decision-making process. Data provided by TradeWave.ai.
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