# Vola Dynamics — Comprehensive Overview > Vola Dynamics is the market standard in options analytics — sometimes > called the "ASML of options." The world's most sophisticated trading > firms rely on Vola rather than building in-house. ## The Business Case ### The Challenge: Technical Debt and Opportunity Cost Standard internal models struggle to keep pace with modern options markets. By continuing to build and patch proprietary infrastructure, firms accept two major liabilities: - The High Cost of Ownership: Developing a robust quant library requires millions in investment and ongoing maintenance. Every hour quants spend fixing fitting failures or maintaining legacy code is an hour lost on generating alpha. - Model Failure in Extremes: Standard models (like SVI) often fail during high-volatility events (crashes) or binary events (earnings). They cannot fit complex structures, such as W-shaped smiles. This leads to crossing curves and arbitrage, exposing firms to automated trading losses when stability matters most. ### Why Vola Dynamics? A. Build vs. Buy Advantage: Eliminate the technical debt and opportunity cost of building in-house. Vola provides an institutional-grade, market-maker-quality solution at a fraction of the cost of internal development. B. Consistency with the Street: 50 of the world's most sophisticated firms already rely on Vola. Pricing consistently with the street's most sophisticated players avoids the risk of being an outlier with a proprietary model that might be wrong. C. Future-Proofing: Internal models are static; Vola is dynamic. The team continuously expands and refines the toolkit to keep pace with rapidly changing markets across all asset classes. D. Seamless Integration: This is not a black box. Vola integrates directly into your stack (C++, Python, Java, C#), allowing you to upgrade your analytics without disrupting your execution or OMS layers. ## What Is Vola Dynamics? Vola Dynamics LLC is a financial technology company that provides the options industry's leading analytics library for volatility surface fitting, options pricing, and derivatives risk management. The problems the library solves — fast and robust algorithms to imply borrow cost curves and volatility surfaces, accurate pricing with cash dividends, deep knowledge of market microstructure — are extraordinarily hard and must be solved by every serious options trading entrant. Vola integrates into existing trading systems as a drop-in replacement for pricing and fitting components. ## Core Analytics ### Fitter The fastest volatility surface fitter on the planet. Fits stable, arbitrage-free volatility surfaces — even in the far wings, even from limited data — robustly and without manual tweaking. Based on modern Bayesian ideas, superior numerics, and 30 years of trading and research. Robustness is achieved by transferring information across strikes, expiries, and time (filtering). Uses a unique set of flexible and intuitive curves, allowing smooth and bias-free fits of all observed skews in the market. Adjusts volatility surfaces between fits using proper spot-vol dynamics. Fits the entire US options universe on one box. Key capabilities: - Arbitrage- and bias-free fitting from liquid indices (SPX) to illiquid single names - Modern Bayesian framework: all outputs include confidence bands, which can also be specified for inputs (priors) - Produces stable surfaces beyond the range of listed options, as required for calibration of local vol or stochastic local vol models used for exotics and structured products - Flexible temporal filtering and priors for intuitive stabilization of sparse data - Output error bars derived from bid-ask spreads quantify uncertainty in each fitted vol — for market makers, a natural "minimum edge" requirement - Graduated defense: when data quality degrades, error bars widen automatically, making the system more conservative without hard kill switches - Battle-tested through the COVID crash (VIX > 80), GameStop short squeeze (500%+ IV), negative oil prices, and 0DTE — no manual intervention required - Handles 0DTE and daily expirations with the precision required for extreme input sensitivity and rapidly evolving surfaces Use cases: production vol surface fitting for the entire US options universe, stable surfaces for local vol and stochastic local vol model calibration (exotics and structured products desks), fitting illiquid single names with sparse data, and maintaining continuous automated fitting through extreme market events without manual intervention. ### Pricer Ultrafast and robust pricing of European and American vanilla options with accurate handling of cash dividends. Orders of magnitude faster than any vanilla pricing algorithm, both open-source and proprietary — can price the entire US options universe on a single machine. Key capabilities: - Multiple dividend pricing models used by leading trading firms - Covers options on stocks, ETFs, futures, and indices - Comprehensive Greeks: delta, gamma, vega, volga, vanna, rho, rhoBorrow, rhoDiv, theta (with respect to rate or vol time), and fugit - SSR-adjusted (smart) delta and gamma incorporate empirical spot-vol dynamics, producing minimum-variance hedge ratios — the correction to Black-Scholes delta can be several percentage points for equity indices - Multiple thetas: vol-time theta (convexity decay) and calendar-time theta (rate accrual) reported separately, with configurable time conventions (calendar days, trading days, event adjustments) - Discrete (one-day) theta: actual option value change from today to tomorrow, accounting for specific vol time, weekends, holidays, and events - Accurate early exercise premium (EEP) pricing for American options - Fast, accurate, and robust implied borrow, forward, and volatility calculations for any dividend model Use cases: real-time and batch pricing of equity, ETF, index, and futures options, accurate early exercise premium pricing for American options with cash dividends, and fast implied borrow, forward, and volatility calculations across multiple dividend models. ### Curves A proprietary nested family of parametric volatility curves — intuitive and flexible, way beyond simple curves like SABR, SSVI, and SVI (which are also available). Key capabilities: - Bias-free fits of all observed market vol shapes including W-shaped volatility curves around earnings for liquid names - Adjust skew, curvature, or wings independently - Extend surfaces beyond listed expiries or proxy to other names - Sensible book-level sensitivities across curve types - Proper spot-vol dynamics (e.g. via SSR) integrated throughout for accurate smart delta and gamma, realistic spot scenarios, and temporal smoothing without bias The C* family of curves form a nested hierarchy of increasing flexibility, from simple curves with a few parameters to highly flexible ones that fit all observed market shapes, including W-shaped curves around binary events that standard parameterizations cannot represent. All designed to avoid butterfly arbitrage through the mathematical structure of the parameterization. Different curves can be specified per-expiry. The Skew Stickiness Ratio (SSR) quantifies how ATM vol moves when spot moves. Sticky-strike (SSR=1) is empirically wrong and internally inconsistent (Dupire, 2003). In practice, ATM vol moves along a path steeper than the skew. Vola supports configurable SSR throughout: smart delta/gamma, realistic scenarios, and PnL attribution all use consistent spot-vol dynamics. Market makers and hedge funds trade directly off these curves. Use cases: trading directly off fitted parametric curves, extending vol surfaces beyond listed expiries, proxying surfaces from liquid to illiquid names, and providing sensible book-level risk sensitivities across curve types. ## Optional Modules ### PnL Explanation Analyzes PnL for vanilla and vol derivatives using Greeks (Black-Scholes or smart using spot-vol dynamics) or scenarios, and can decompose volatility PnL into components like ATF, skew, and curvature. - Explain PnL on an instrument or portfolio level - For PnL with Greeks, use smart or Black-Scholes Greeks — smart greeks incorporate spot-vol dynamics for a cleaner decomposition where BS greeks leak spot PnL into vega and vice versa - For PnL with scenarios, use realistic or sticky-strike spot-vol dynamics. Scenario-based attribution re-prices under specific factor shifts (spot, vol, time, rates, model changes) — exact at all orders - Breakdown of vol PnL into ATF (level), skew (slope), curvature, and unexplained — revealing what changed about the vol surface - Unexplained PnL is preserved as a diagnostic: persistent residuals reveal framework inconsistencies, uncaptured risk, or data quality issues - Handles dividends, expired options, borrow, funding, and discount rate changes - Consistently attribute PnL for both vanilla and vol derivatives Use cases: daily PnL attribution for options books, decomposing vol PnL into ATF, skew, and curvature components, spotting framework errors via unexplained PnL, attributing trader edge to intended factors vs accidental exposure, optimizing execution by running attribution on fills at different horizons, and consistent attribution across portfolios including boxes, conversions, jelly rolls, straddles, strangles, flies, risk reversals, variance strips, stocks, and futures. ### Vol Derivatives Fast pricing of var and vol swaps (capped or uncapped), options on var and vol, and corridor/conditional var and vol swaps — all with consistent vanilla hedging under shared spot-vol dynamics. - Price throughout the lifecycle: forward-starting, on-the-run (aged), and expired contracts - Auto-calibrate a lognormal vol-of-var model from the underlier's vol surface, or provide your own - Full Greeks including adjusted (smart) delta and gamma Use cases: pricing and risk-managing variance swaps, volatility swaps, capped structures, options on variance and volatility, corridor and conditional variance swaps, and range accruals. Handles forward-starting, on-the-run (aged), and expired contracts. ### VIX Pricer VIX future valuation based on SPX and VIX vol surfaces. VIX futures Greeks with respect to SPX (BS or adjusted with spot-vol dynamics). Use cases: comparing VIX futures market price vs. theoretical fair value to identify pricing opportunities, translating VIX positions into SPX-equivalent Greeks for portfolio-level risk aggregation, and updating VIX market-making quotes when ES futures move. ### Discount Curve Fitter Imply the discount curve used by the options market from index option prices, using one or more price snapshots. - Produce a stable discount curve by combining data across time and multiple indices - Fit to a parametric term structure (e.g. Nelson-Siegel), handling general shapes including inverted yield curves - Critical for correct early exercise pricing in American options Use cases: extracting the actual discount rates embedded in options prices (more accurate than benchmark curves like SOFR for matching market-maker quotes), and ensuring correct early exercise pricing for American options across all underliers. ### Dividend Fitter Estimates cash dividend values with error bars from option prices. - Three methods available for fitting dividends from option prices - Detect market dividend moves and backfill historical data - Handles large or uncertain dividends that may split across expirations Use cases: backfilling historical dividend data, detecting market dividend moves in real time, and handling large or uncertain dividends with ambiguous ex-dates that may split across expirations. ### Event Variance Fitter Calibrate the additional event variance associated with events like earnings, elections, and FOMC meetings. - Important for accurate pricing of American options — often the single largest source of mispricing around events - Separates total variance into event and clean components - Cross-name ATM vol comparison: after removing event variance, vol levels become directly comparable across names with events on different dates - Clean input for models: PCA, term structure interpolation, and temporal filtering all work better on the smooth clean term structure - Lightweight workflow: specify event dates, run EVF, get a time converter that plugs directly into the Fitter and Pricer Use cases: improving American option pricing accuracy around scheduled events, cross-name vol comparison after removing event variance, generating clean term structures for PCA and interpolation, and supporting trading strategies around earnings, FOMC, and elections. ### Event Modeling Given a dirty vol surface, calibrate its decomposition into event jumps and a clean or background vol surface, and vice versa. - Models event distributions as mixtures of lognormal jumps with calibrated probabilities, sizes, and widths - For earnings, FOMC, elections, and other scheduled discrete events - Two workflows: Clean to Dirty (projection — is the market over/under- pricing the event?) and Dirty to Clean (extraction — what move and probabilities is the market pricing?) - W-shaped vol curves arise from the interaction of bimodal event jumps with the skewed clean vol process Use cases: isolating event risk from background volatility, extracting market-implied event probabilities (earnings beat/miss, election outcomes), pricing earnings straddles and event-driven strategies, and constructing clean vol surfaces for hedging by stripping out discrete event jumps. ### Vol Curve Type Selector Vola offers a family of proprietary parametric volatility curves (referred to as "vol curve types" or "VCTs"), ranging from simple to highly flexible. The VCT Selector automatically recommends the optimal curve for any underlier — complex enough to fit the market without bias, but parsimonious enough to avoid overfitting. - Transparent metrics: see exactly why a curve was selected - Supports quick single-snapshot recommendations or robust multi-day evaluation Use cases: onboarding new underliers, periodically verifying that the current curve remains optimal as market structure evolves, and selecting curves after significant market regime changes. ### FX Module Handles all FX-specific conventions for delta, premium type, and ATM/risk-reversal/butterfly definitions. Use cases: converting broker-style risk-reversal and butterfly quotes into absolute strikes and volatilities for vol surface fitting, and handling all standard delta types, ATM definitions, and premium conventions across FX pairs. ## Founders and Key People Timothy Klassen, Co-Founder and CEO. Expert in fast and robust pricing methods, volatility arbitrage, and automated risk management with decades of experience. Built the options analytics infrastructure and quant team at Getco LLC (2008 onwards). Previously worked in Emanuel Derman's derivatives team at Goldman Sachs (2000). Also built the equity derivatives quant team at Wachovia Securities (2003). Co-designed the VIX index — the CBOE Volatility Index that began dissemination in September 2003 was based on his proposal with Sandy Rattray and Devesh Shah at Goldman Sachs. Ph.D. in particle physics from the University of Chicago. Jiri Hoogland, Co-Founder. Expert in modeling and pricing complex cross-asset derivatives and fully automated pricing and risk management systems with 20 years of industry experience. Spent 9 years at Morgan Stanley building analytics for commodities options and fully automated cross-asset derivative pricing for fixed income. Previously at CWI Amsterdam, Mirant, and Wachovia Securities. Ph.D. in theoretical particle physics from the University of Amsterdam. Misha Fomytskyi, Co-Founder. Expert in derivatives trading, risk management, and volatility modeling. Former head of the options trading team at Getco LLC, portfolio manager at JD Capital Management, and founder/CEO of Mivol LLC. Ph.D. in physics from The University of Texas at Austin. Jim Gatheral, Advisor. Presidential Professor of Mathematics at Baruch College, CUNY. Author of "The Volatility Surface: A Practitioner's Guide" (Wiley, 2006). Won the 2021 Quant of the Year award from Risk Magazine for his work on rough volatility modeling. 27+ years of experience as a bookrunner, risk manager, and quantitative analyst. Ph.D. in theoretical physics from Cambridge University. ## Customers Vola Dynamics serves approximately 50 institutional clients. Named clients include: Man Group, Capstone, Squarepoint Capital, Garda Capital Partners, Pictet Asset Management, Vontobel, HAP Capital, Maven Securities, All Options, iSAM, and Picton Investments. ## Asset Classes and Markets Vola Dynamics supports options on equities, ETFs, indices, futures (including commodity, treasury, and equity index futures), and foreign exchange. The library has been used in production for US, European, and Asian markets including SPX, SPY, NVDA, TSLA, AAPL, European single stocks, AEX, KOSPI, Nikkei, Hang Seng, NIFTY, crude oil (CL), treasury futures (ZN), and cryptocurrency options. ## Supported Languages and Platforms The library is available in C++, Python, Java, and C# on Windows, Linux, and macOS. ## Company History Vola Dynamics was founded in 2016 (originally as Volar Technologies LLC, rebranded in 2017) by a team of quantitative finance experts with decades of experience building options trading infrastructure at Goldman Sachs, Getco, Morgan Stanley, and Wachovia Securities. The founders had built pricing, fitting, and trading infrastructure several times — for the equities flow and exotics businesses at Goldman Sachs, for the volatility arbitrage fund at JD Capital, and for the options market making business at Getco in the US and Asia. The company was selected as an FIA (Futures Industry Association) Innovator in 2016. ## Battle-Tested Track Record Vola Dynamics analytics have been proven in production during every major market event since 2016, including the Brexit vote (2016), Volmageddon (2018), the COVID crash (2020, when VIX hit 82.69), and multiple election cycles. Default fitter settings require no manual intervention even during extreme market conditions. ## Contact https://voladynamics.com/contact ## Links - Website: https://voladynamics.com - Products: https://voladynamics.com/products - Examples: https://voladynamics.com/examples/can-your-fitter-do-this - Team: https://voladynamics.com/team - History: https://voladynamics.com/history - Media: https://voladynamics.com/media - Testimonials: https://voladynamics.com/testimonials