Skip to content
Roadmaps / Pricing and derivatives

Volatility Quant

Builds the implied volatility surface, forecasts realised volatility and tests whether selling or buying volatility pays.

The job

A volatility quant supports an options desk or a volatility fund. They build the implied volatility surface, forecast realised volatility, design delta-hedging rules, attribute the book’s P&L to its Greeks, and run the backtests that say whether selling or buying volatility pays. The desk’s P&L is roughly implied minus realised volatility, less hedging and trading costs. Things go wrong with jumps the hedge cannot follow, surfaces with hidden arbitrage, and backtests that count fills which could never have happened.

Supports
Options desks and volatility funds
Stages
6 to learn, then proof of work
Two routes
Free resources, or SelfMadeQuant programmes and projects

The roadmap

The same stages either way. The free route uses university courses, lectures, books and papers that are free online. The SelfMadeQuant route maps each stage to our programmes and ends in projects on real market data.

  1. Stage 01 · Foundations

    Probability and statistics

    Distributions, estimation and telling noise from an effect.

    • Distributions and moments
    • Maximum likelihood
    • Confidence intervals
    • Hypothesis tests

  2. Stage 02 · Foundations

    Options and no-arbitrage

    What an option pays, what it must be worth, and what its Greeks measure.

    • Payoffs and put–call parity
    • Binomial model
    • Black–Scholes
    • Greeks

  3. Stage 03 · Core

    Computational pricing

    Pricing what has no formula, and knowing how accurate the answer is.

    • Monte Carlo
    • Variance reduction
    • Finite differences

  4. Stage 04 · Core

    Realised volatility and forecasting

    Measuring volatility from data and forecasting it out of sample.

    • Realised variance
    • GARCH
    • HAR model

  5. Stage 05 · Specialist

    The smile and stochastic volatility

    Fitting a surface without arbitrage and the models that explain its shape.

    • Implied vol surface
    • SVI
    • Heston
    • Variance swaps

  6. Stage 06 · Specialist

    Hedging and trading costs

    Delta hedging in discrete time, and whether the size is there to trade.

    • Discrete delta hedging
    • Hedging error
    • Transaction costs

  7. Stage 07 · Proof of work

    Build a crypto option smile and test the variance risk premium

    Pull daily BTC option chains from the Deribit public API, fit SVI to each expiry and check the fitted surface for calendar and butterfly arbitrage. Then compare 30-day implied variance with the realised variance that followed to measure the variance risk premium, and contrast it with the VIX.

    Free data to start with

Practise the interview

Questions this role gets asked, each with a hint and a worked solution.

  1. Why does a stochastic-volatility model produce a smile when Black–Scholes does not?
  2. A delta-hedged long call makes money on a big move either way. Why is that not free money?
  3. Implied or historical volatility: which is the better forecast, and when would you trust each?

Start the SelfMadeQuant route free: a free account opens one complete topic from each primer and the interview question bank. No card needed.

Start free