New Volland Technical Paper: Influence of Options MM Risk Management on the Implied Vol Surface

Episode Overview

• Explores how option order flow and market maker risk management shape the implied volatility (IV) surface across different expiration cycles.
• Investigates the distinct Greek drivers of market maker behavior, contrasting Gamma-dominant 0DTE options with Vanna- and Vomma-sensitive longer-dated expirations.
• Analyzes mathematical frameworks like the SVI-JW model and the NetChange metric to quantify market maker stress and predict volatility events.
• Decodes how retail order flow (such as OTM put buying) triggers feedback loops, shaping market skew and offering counter-intuitive trading opportunities.

Key Concepts

• Locality of Option Order Flow: Buying an option with a specific delta (e.g., a 15 delta call) has a localized impact on the implied volatility (IV) surface in that specific region rather than shifting the entire surface uniformly.
• Greek Specificity across Expirations: Option surface dynamics are governed by different Greeks depending on the time to expiration (DTE). For Zero Days to Expiration (0DTE), Gamma is the primary driver of IV and delta changes because contract payouts behave binarily and Vega sensitivity is minimal. For 30–60 DTE, Vanna (sensitivity of delta to volatility) and Vomma (sensitivity of vega to volatility) become the dominant drivers because longer-term options are highly sensitive to volatility changes while their deltas remain relatively stable.
• SVI-JW Model: The Stochastic Volatility Inspired Jump Wings (SVI-JW) model fits a volatility curve using five parameters (at-the-money variance, at-the-money slope, wing slopes, and minimum variance), allowing quantitative traders to track high-frequency changes on the IV surface while remaining arbitrage-free.
• Realized Volatility Regimes: Volatility environments are classified as high or low by comparing the 30-minute realized volatility level against its 20-day rolling median, allowing traders to contextualize the significance of order flow impact.
• Dealer Feedback Loops and Vol Events: When out-of-the-money (OTM) puts move in-the-money (ITM), dealer Vanna and Vomma exposures converge toward zero. This decay of second-order risk exposures forces market makers to aggressively adjust their hedges, which can trigger a volatility spike. Conversely, once the market stabilizes and bounces, Vomma and Vanna normalize, causing IV compression that mechanically fuels a market recovery.
• The NetChange Metric: NetChange serves as a proxy for market maker Greek stress by measuring the 1-minute change in second-order Greek exposures (soft deltas), weighted by their current exposure levels. In regimes of elevated realized volatility, NetChange explains roughly 8% of At-The-Money (ATM) implied volatility variance, which is a highly significant predictive edge in financial markets.

Quotes

• At 3:38 - "Buying a 15 delta call is going to impact that region of the implied volatility surface. This is what I wanted to test... to first make sure... how the implied volatility surface functions." - Explaining the hypothesis that order flow has localized, rather than uniform, effects on the IV surface.
• At 5:12 - "On 0DTE, probably Gamma is going to matter the most... and then for the longer expirations, the expectation was that it's going to be Vanna and Vomma... affecting your vega book." - Detailing which Greeks drive market maker risk pricing depending on the expiration timeline.
• At 5:35 - "I went for second-order Greeks, not first-order Greeks, because in my belief it is really the change of the first-order Greeks that impacts how you're going to quote the surface." - Providing the theoretical basis for studying second-order risk exposures rather than standard delta or vega.
• At 8:24 - "Through this SVI model, it becomes easy to track changes in the implied volatility surface." - Highlighting the utility of the SVI-JW parametrization for quantitative analysis of the surface.
• At 17:51 - "My theory behind that is that you have a higher spot-vol correlation position in your put side than in your call side. So as a market maker, you need to manage that more aggressively and you become more averse of inventory." - Explaining why put-side transactions have a much stronger impact on skew than call-side transactions.
• At 20:39 - "Pricing 0DTE is probably quite different from pricing longer-term options... It’s very much about your Gamma and getting in the money. You do have jump vol and Vega in 0DTE, but it’s not very strong, so it’s very much about your deltas and how your deltas change." - Explaining why Gamma dominates 0DTE options while Vega takes a back seat.
• At 21:32 - "As puts move ITM, both Vanna and Vomma converge toward zero, which can lead to a vol event... Volatility events create a high likelihood of a market reversal. Following the bounce, Vomma becomes negative again and Vanna turns positive, leading to IV compression and reinforcing the rebound." - Explaining the mechanical feedback loops that drive dramatic market capitulations and subsequent recoveries.
• At 21:50 - "NetChange is... a proxy for market maker Greek stress. So, their second-order positioning moving for or against them, lowering or getting higher in absolute terms." - Defining the NetChange metric as an aggregate gauge of structural market pressure.

Takeaways

• Differentiate Trading Strategies by DTE: Focus strictly on Gamma exposure when modeling intraday or 0DTE options, whereas Vanna and Vomma metrics should be utilized to assess risk and position for longer-dated (30-60 DTE) option trades.
• Monitor Put Buying to Anticipate Skew Shifts: Track retail and institutional demand for out-of-the-money puts, as this specific flow aggressively steepens the volatility skew due to market makers adjusting for downside inventory risk.
• Exploit Flattening Skew with Near-the-Money Spreads: In high-stress, negative Gamma regimes where ATM volatility spikes and skew flattens, look to sell put verticals near-the-money rather than far out-of-the-money to take advantage of structurally mispriced premiums.
• Incorporate "NetChange" to Gauge Market Stress: Implement the NetChange metric as an early warning system for market maker stress during high realized volatility regimes, using it to anticipate rapid dealer hedging and potential market reversals.
• Beware of Inelastic Call Skew Fragility: Avoid over-reliance on static call skew models during periods of intense call buying; when market makers are caught offside without proper call-side protection, it can lead to explosive upside squeeze dynamics.