CQF study guide
CQF · Work through your exam, one topic at a time.
The Certificate in Quantitative Finance (CQF) is described by its provider as "The world's leading master's-level professional qualification in quant finance." It is awarded by the CQF Institute, part of Fitch Learning, and is delivered by Fitch Learning. The programme is taught online and part-time over six months, with content spanning quantitative finance and machine learning, taught by industry practitioners rather than purely academic staff. The provider states the qualification has been shaping quant careers for 23 years, with 11,500+ alumni and delegates in 90+ countries. It is aimed at working finance professionals, analysts, developers and risk practitioners who want to apply practical quant techniques in industry rather than pursue a research degree; the provider also markets it to employers sponsoring teams. The next programme starts 27th January 2027, and a live online information session is scheduled for 19th November 2026. Candidates should note that the provider's assessment and FAQ pages were unavailable when this profile was compiled, so exam format, question counts, duration, pass mark, fees and retake policy are not confirmed here and should be checked directly with the provider. Topics on this exam: Quantitative finance and machine learning. The CQF study guide has 1 section and about 860 words.
01Quantitative Finance: Risk-Neutral Pricing and Ito Calculus
The Quantitative Finance domain tests whether you can move fluently between three equivalent descriptions of a derivative's value: a risk-neutral expectation, a partial differential equation, and a numerical scheme. CQF questions typically probe the links between these views rather than routine formula substitution. Under the risk-neutral measure Q, the discounted asset price is a martingale: e^{-rt}S t = E^Q[e^{-rT}S T F t]. Pricing is an expectation under Q, not a forecast under the real-world measure P.
860 words, about 4 minMore study resources for CQF
- Practice questionsIn a Kalman filter applied to a linear state-space model, what does the innovation (measurement residual) sequence represent under correct model specification?
- FlashcardsBlack-Scholes formula. Call price C = S·N(d1) − K·e^(−rT)·N(d2), where d1 = [ln(S/K) + (r + σ²/2)T]/(σ√T) and d2 = d1 − σ√T; assumes lognormal prices, constant volatility, no arbitrage and continuous trading.
- Cheat sheetBlack-Scholes call price: C = S·N(d1) − K·e^(−rT)·N(d2), with d1 = [ln(S/K) + (r + σ²/2)T] / (σ√T) and d2 = d1 − σ√T; put via put-call parity P = C − S + K·e^(−rT).
- Key termsItô's lemma. The chain rule for functions of a stochastic process driven by Brownian motion.
- Study planBuild a Python environment with numpy, pandas, scipy, statsmodels and scikit-learn, and verify each import runs.
- Common mistakesUnder exam time pressure the mechanics of building the tree or simulating paths absorb attention, and the measure/discounting step is treated as an afterthought.
- Exam-day guideBring the exact ID type and name spelling your registration used.