What are flies in options volatility?

The core idea

Fly removes the skew direction and keeps the smile curvature.

Risk reversal asks which wing is richer. Fly asks whether the wings together are richer than the middle of the smile. That makes it useful for monitoring convexity, event premium, and wing quote quality across the volatility smile and volatility surface.

A positive fly means the average of the two wings is above ATM. A negative fly means the average wing volatility is below ATM under the chosen convention.

Formula

Average the put and call wings, then subtract ATM volatility.

The most common volatility fly quote uses the same delta on both wings and the same expiry. This guide uses 25-delta nodes.

25-delta fly definition

A 25-delta fly is an expiry-matched wing average minus ATM volatility.

The useful definition is narrow because the metric is a risk node, not a loose description of curvature. Derivasys reads the 25-delta put, 25-delta call, and ATM level from the same fitted SVI smile, then publishes the fly beside risk reversalsand quote-through-fit diagnostics.

Construction workflow

A fly node is reproducible only when delta bucket, ATM source, and smile fit are explicit.

A fly looks simple, but production systems need strict node definitions. The 25-delta put, 25-delta call, and ATM level should come from the same accepted smile, usually the fitted SVI slice. If one wing is interpolated or extrapolated, that quality flag should travel with the published fly.

Worked example

If the wings average 60.5% and ATM is 56%, the fly is 4.5 vol points.

With a 65% put, a 56% call, and 56% ATM volatility, the average wing is 60.5%. The fly is 60.5% - 56.0% = 4.5 volatility points.

Interpretation

Fly and risk reversal together separate curvature from signed skew.

A fly should almost never be read alone. The same fly value can hide very different put-wing and call-wing shapes. Pairing it with the risk reversaland the underlying smilemakes it clear whether wings are rich together, one wing is moving directionally, or the SVI fit is reacting to sparse data.

Term structure

A fly term structure separates short-dated event convexity from broad surface curvature.

One expiry fly is a smile curvature node. The fly term structure compares that node across maturities so a desk can see whether wing richness is concentrated around one event or is repricing the whole surface.

The same total-variance context used in forward volatilityhelps decide whether a fly move is local to one expiry or part of a broader term-structure change.

Dashboard usage

Flies help separate skew from curvature.

A surface can have the same risk reversal but a different fly. That distinction matters when traders are watching wing demand, event convexity, and whether an SVI fit is overreacting to sparse wing quotes. The risk reversal guide covers the signed-skew side of the same smile.

Dashboard workflow

Fly monitoring works best beside skew, SVI residuals, and quote quality.

A fly can move because both wings repriced, because ATM moved, because one wing quote went stale, or because the fitted smile changed shape. A production dashboard therefore treats flies as surface diagnostics, not just a static number.

Fly changes also matter for variance swap and Greek workflows because wing richness changes implied variance and vega distribution across the smile.

API conventions

Fly data needs delta, ATM, source, and quality labels.

A volatility fly API field should make the node reproducible: expiry, delta bucket, ATM source, units, source surface, timestamp, and whether the node used live quotes, interpolation, or extrapolation.

{
  "metric": "FLY25",
  "expiry": "2026-09-25",
  "deltaBucket": "25-delta",
  "atmSource": "fitted_svi_atm",
  "units": "vol_points",
  "value": 4.5,
  "source": "fitted_svi_surface",
  "sourceQuality": "live_quotes_plus_interpolation",
  "timestamp": "2026-07-03T12:00:00Z"
}

A volatility fly API should also expose fly interpolation share, fly replay id, and risk-node age so API consumers can decide whether the curvature node is quote-supported or model-heavy.

Monitoring

25-delta fly monitoring should track node age, wing support, residuals, and replay state.

A fly can look current while one wing is stale or extrapolated. Production monitoring should therefore track the fly as its own risk node, with provenance from the accepted smile and the quote-state records behind each wing.

This is the same risk-node health pattern described in the production monitoring article and the order-book construction article.

Quote provenance

Fly moves should be traced back to wing quotes, interpolation, and delta remapping.

In crypto options, a fly can move because a wing traded, because the forward moved, or because the fitted curve remapped strikes into a different delta bucket. Derivasys keeps fly nodes tied to quote-through-fit diagnostics so curvature changes can be reviewed before they are used in risk or API workflows.

Failure modes

Fly nodes fail when ATM source, delta mapping, wing support, or surface versions are hidden.

The most dangerous fly is a clean-looking number whose inputs cannot be reproduced. A dashboard should make the failure mode visible before the node feeds risk, variance, or API workflows.

Dashboard screenshots

Derivasys shows flies beside risk reversals and surface diagnostics.

Fly panels are most useful when they stay connected to the fitted smile and quote diagnostics. The same dashboard context explains whether a fly move reflects real convexity demand or a weak wing quote.

Reading path

Move from smile curvature into the full risk-node workflow.

• Implied volatility: Start with the accepted IV points that feed each wing and ATM node.
• Volatility smile: Start with the one-expiry smile whose wings and ATM point define the fly.
• Risk reversals: Pair fly curvature with signed skew from same-delta call-minus-put volatility.
• SVI: Use a fitted total-variance smile to read stable same-delta nodes.
• SSVI: Check whether fly behavior is coherent across the full surface.
• Sticky strike vs sticky delta: Decide whether fly scenarios keep listed strikes or delta buckets fixed.
• Variance swaps: Connect wing richness and curvature to implied-variance estimates.
• Forward volatility: Compare fly term structure with forward variance buckets.
• Production monitoring: Monitor stale risk nodes, wing support, and fly replay state.
• Order-book construction: Trace fly inputs back to quote-state events and wing venue marks.

References

Related Derivasys guides.

• Derivasys volatility surface guide
• Derivasys implied volatility guide
• Derivasys volatility smile guide
• Derivasys risk reversal guide
• Derivasys option Greeks guide
• Derivasys variance swaps guide
• Derivasys SVI guide
• Derivasys SSVI guide
• Derivasys forward volatility guide
• Derivasys order-book construction article
• Derivasys production monitoring article
• Derivasys sticky strike vs sticky delta guide