- Event volatility is the incremental variance attributed to a known catalyst and can be extracted from option-implied variances across expiries.
- Use a before-and-after expiry pair or model-free variance extraction to isolate event variance, then convert to an implied jump magnitude.
- Calendar spreads, variance swaps, and forward-variance constructions let you trade or hedge discrete risk while controlling baseline exposure.
- Account for skew, liquidity, early exercise, and non-symmetric jumps; validate with multiple expiries and option-based variance integration.
- Practical implementation requires vega weighting, delta-hedging, stress scenarios, and explicit stop rules for rapid volatility moves around catalysts.
Introduction
Event volatility term structures refer to the decomposition of implied volatility into a baseline, time-distributed component and a discrete component tied to a known near-term catalyst, such as earnings, FDA decisions, or trial rulings. In plain terms, you want to know how much of option prices reflects regular market noise and how much is a single predictable shock. Why does that matter to you? Because separating those pieces changes how you size trades, hedge risk, and estimate expected moves around the event.
In this article you'll learn a step-by-step framework to extract event volatility from option prices, convert that variance into an implied move, and build tradable constructs that isolate event risk. We'll cover the math in intuitive terms, show practical examples using $AAPL and $PFE style catalysts, and highlight implementation traps to avoid. Ready to quantify discrete risk instead of guessing at it?
Fundamentals: Baseline Volatility vs Event Volatility
Baseline volatility is the continuous component of future realized variance that accumulates smoothly over time. Traders often treat it as the expected annualized variance driven by market dynamics, macro news, and stock-specific noise. Event volatility is a concentrated increment of variance assigned to a known-date catalyst. It's not spread evenly across days, it is concentrated, and it changes the term structure discontinuously.
Two core properties you should remember: implied variance scales linearly with calendar time, and variance contributions are additive. That means implied variance for a maturity T equals integrated forward variance over 0 to T plus any discrete jump variance from events within that horizon. You can exploit that linearity to strip out the event contribution from option-implied variances.
Term-Structure Decomposition: Practical Methods
You have several ways to extract event variance. Pick the one that matches your data and liquidity constraints. Below are three practical methods, ordered by simplicity and robustness.
1. Two-Expiry Calendar Extraction (simple and common)
Choose an expiry before the event, with implied volatility sigma_pre and maturity T_pre (in years). Choose an expiry after the event, with implied volatility sigma_post and maturity T_post. If you assume baseline forward variance is roughly constant between the two expiries, event variance j (in squared returns units) is:
j = sigma_post^2 * T_post - sigma_pre^2 * T_post
Intuition: sigma_pre^2 approximates baseline variance. Multiply by T_post to get expected baseline variance through the later expiry. The difference yields variance attributable to the event. The jump standard deviation is sqrt(j). For example, if sigma_pre = 30% and sigma_post = 45% with T_post = 0.25 years, j = (0.45^2 - 0.30^2) * 0.25 = 0.028125, and implied jump stdev ≈ 16.8%.
2. Multi-Expiry Forward-Variance Fit (more robust)
Use multiple expiries to fit a piecewise constant forward variance curve. Solve the linear system where for each expiry i, sigma_i^2 * T_i = sum_k f_k * delta_k + sum of event variances for events inside that expiry. This gives you forward variance segments f_k and discrete jump variances simultaneously when you include expiries that straddle events. This approach reduces noise from skew and allows you to test stability across expiries.
Practically, pick 4 to 8 expiries spanning before and after the event. Regularize the fit to avoid overfitting noise. If you have liquid variance swaps, include their quoted variances as additional constraints. The output is a forward-variance curve and isolated event variances.
3. Model-Free Variance Integration (most accurate but data-heavy)
Use the model-free implied variance formula, integrating option mid-prices across strikes for each expiry. That yields sigma_i^2 * T_i in a way that is robust to skew. Then apply the same subtraction logic as above: event variance = integrated variance for post-event expiry minus baseline integrated variance extrapolated over the same horizon. This method minimizes bias from ATM-only measures and is preferred when you have full option chains and good liquidity across strikes.
Converting Event Variance to an Implied Move and Pricing
Once you have event variance j, convert it to an implied move. The implied standard deviation of the jump is sqrt(j). That number is an annualized percent move expressed in decimal return units. In practice event traders quote an implied absolute move for the underlying, for example 12% expected one-day jump. If you want a one-day equivalent, interpret sqrt(j) as the magnitude of the discrete return associated with the event, since j is a return-variance unit rather than a time-smoothed variance.
Be explicit about assumptions. The simple two-expiry extraction assumes the pre-event expiry accurately captures baseline variance. If baseline forward variance is changing materially because of macro risk or other events, you should use the multi-expiry fit. Also remember skew: options at different strikes embed the market's asymmetric view. Extracting event variance via integrated variance preserves skew information while ATM measures can misstate the implied jump magnitude for directional moves.
Real-World Examples and Numbers
Example 1, earnings for $AAPL: imagine 30-day IV before earnings is 28% for a pre-event expiry and 42% for a 90-day expiry that includes the earnings date. Let T_post = 0.25 years. Using the two-expiry method, j = (0.42^2 - 0.28^2) * 0.25 = (0.1764 - 0.0784) * 0.25 = 0.098 * 0.25 = 0.0245. The implied jump stdev is sqrt(0.0245) ≈ 15.7%. That tells you the market is pricing a one-off earnings shock of roughly 16% standard deviation, which you can compare to historical earnings moves to judge reasonableness.
Example 2, FDA decision for $PFE: suppose the 30-day integrated variance yields sigma_pre = 45% and the 120-day integrated variance yields sigma_post = 58% with T_post = 0.33. j = (0.58^2 - 0.45^2) * 0.33 = (0.3364 - 0.2025) * 0.33 = 0.1339 * 0.33 = 0.0442. Jump stdev ≈ 21.0%. That corresponds to a large, binary outcome market expects for a pivotal regulatory event.
These numbers are hypothetical but realistic. They show how event variance can dwarf baseline variance and why it matters for sizing and hedging. You should always cross-check against historical realized moves and implied moves derived from straddle prices for sanity.
Execution Strategies and Risk Management
Once you isolate event volatility, you can design trades that target the event bucket while minimizing baseline exposure. Common approaches include buying or selling short-dated straddles that expire after the event, implementing calendar spreads that are long after-event volatility and short pre-event volatility, or using variance swaps to isolate variance-years exposure. Delta-hedge consistently to neutralize directional risk.
Vega-weighting is essential. When you use calendar spreads, weight your positions so net vega on baseline buckets cancels and residual vega aligns with the event variance you want to trade. Stress-test positions across ±1 to ±3 sigma event moves and check margin and gamma risk. Liquidity can evaporate around catalysts, so ensure execution plans and slippage budgets are realistic.
Common Mistakes to Avoid
- Relying solely on ATM IV: ATM numbers ignore skew and can misstate the event variance. Use integrated variance or multiple strikes to capture asymmetry.
- Assuming symmetric jumps: many catalysts produce asymmetric outcomes. Check put-call skew and use strangles or skew-sensitive structures if necessary.
- Ignoring liquidity and execution timing: option widths widen into events, so backtest slippage and avoid overstating theoretical returns.
- Failing to hedge delta and gamma: event trades can produce rapid directional P&L swings. Plan delta-hedges and adjust intraday as needed.
- Using a single expiry pair without validation: volatility estimates can be noisy. Validate event variance across several expiry pairs and with model-free integration.
FAQ
Q: How do I choose expiries to isolate an event?
A: Pick one expiry that falls cleanly before the event and one that falls after. Prefer expiries where option chains are liquid and where the pre-event expiry is close enough that it reflects current baseline variance. Validate with additional expiries if available.
Q: Can I use ATM implied volatility alone to price an event?
A: ATM IV is a convenient shortcut but it ignores skew and can misestimate the event's asymmetric risk. Model-free integrated variance is preferable when accuracy matters.
Q: How do I handle multiple events in a single term structure?
A: Use multi-expiry forward-variance fitting and include separate event variance terms for each known date. The system of linear equations will let you isolate each event if you have enough independent expiries.
Q: Is the extracted event variance the same as the market's probability-weighted outcome?
A: Not directly. Event variance measures expected squared move, not probability. A large variance could reflect a high-probability moderate move or a low-probability extreme move. Use additional information, like implied skew and binary option prices, to infer asymmetry and tail expectations.
Bottom Line
Decomposing implied volatility into baseline and event components turns guessing into measurable quantities. By using calendar spreads, forward-variance fits, or model-free variance integration you can extract event variance, convert it to an implied move, and build hedges or trades that target discrete risk. Remember to validate across expiries, respect skew and liquidity, and maintain active hedging and stress testing.
Next steps: try this on a liquid, well-known catalyst like an earnings release for $AAPL or an FDA date for $PFE. Build the two-expiry extraction first, then expand to multi-expiry and model-free methods to improve precision. At the end of the day, isolating event volatility gives you a clearer view of risk and a better foundation for trading and risk management.
