D'Math University | Finance & Actuarial Mathematics

MSc Quantitative Finance

A professional-oriented postgraduate programme with strong industry partnerships and a curriculum that balances quantitative rigour with real-world finance. Covering everything from machine learning in finance to high-frequency trading mathematics, with a 94% employment rate within three months of graduation.

Postgraduate 1 Year Industry-Linked City & Wall Street
12
Specialist Modules
£75k
Average Starting Salary
42+
Industry Partners
94%
Employment in 3 Months
Programme Overview

Quantitative Finance in Practice

The MSc Quantitative Finance is designed for students who want the highest-calibre quantitative training with a strong emphasis on professional application. The programme covers the full spectrum of modern quantitative finance — from derivatives and fixed income to machine learning, volatility modelling, and regulatory risk analytics.

  • Core Theory: Fixed income, equity derivatives, stochastic modelling, and econometric techniques with rigorous mathematical treatment.
  • Emerging Topics: Machine learning for alpha generation, high-frequency trading mathematics, and alternative data analysis.
  • Regulatory Finance: Basel III/IV capital requirements, FRTB, IBOR transition, and modern risk analytics frameworks.
  • Capstone Project: A practitioner capstone project conducted with an industry sponsor, presenting findings to a panel of finance professionals.

Industry-First Design

Every aspect of this programme has been designed in close consultation with our 42+ industry partners, including Goldman Sachs, Barclays, Citadel, Man Group, and JP Morgan. The result is a curriculum that precisely matches the skills employers seek — and a graduate cohort that is immediately job-ready.

  • Practitioner Faculty: Over 60% of teaching hours are delivered by current quant practitioners on secondment from partner firms.
  • Live Trading Simulations: Weekly live market simulations using Bloomberg and Refinitiv Eikon data platforms.
  • Networking Events: Monthly industry dinners and quarterly careers fairs exclusively for MSc QF students with partner firms.
  • Interview Guarantee: All students who maintain academic standing receive guaranteed first-round interviews at 10+ partner firms.
  • Python & ML Bootcamp: Intensive pre-term Python and machine learning bootcamp for students from non-programming backgrounds.
Course Catalogue

Click any course to view its objective and learning outcomes.

QFN 501 Stochastic Calculus +

Objective

To apply Itô calculus to financial modelling.

Learning Outcomes

  • Apply Brownian motion and Itô's lemma.
  • Solve SDEs.
  • Apply Girsanov's theorem.
  • Use Feynman-Kac formula.
  • Apply martingale representation.
Interactive Activity — Derivative as Slope of Tangent
Drag the slider to move point P along the curve. The tangent line updates — its slope is the derivative.
f(x): x = 1.00
Interactive Activity — Riemann Sum Approximation
Drag the slider to add more rectangles. Watch the approximation converge to the true integral.
Rectangles n = 8
QFN 502 Derivatives Pricing +

Objective

To price European, American and exotic derivatives.

Learning Outcomes

  • Apply Black-Scholes-Merton.
  • Use binomial trees.
  • Price barrier and Asian options.
  • Apply local vol and stoch vol.
  • Calibrate to market data.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
Interactive Activity — Black-Scholes Option Pricer
Adjust spot price, strike, time to expiry, volatility and risk-free rate. The activity computes the European call and put prices plus all five Greeks (Δ, Γ, Θ, ν, ρ).
σ (vol): 25.0% r (rate): 5.00%
QFN 503 Fixed Income +

Objective

To value fixed income securities and interest rate derivatives.

Learning Outcomes

  • Apply yield curve construction.
  • Use Vasicek and HJM models.
  • Apply LIBOR Market Model.
  • Price swaptions and caps.
  • Manage interest rate risk.
QFN 504 Credit Risk +

Objective

To model credit risk in fixed income portfolios.

Learning Outcomes

  • Apply structural and reduced-form models.
  • Price CDS.
  • Use CDO pricing.
  • Apply correlation models.
  • Discuss CVA and counterparty risk.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
QFN 505 Quantitative Risk Management +

Objective

To quantify and manage portfolio risk.

Learning Outcomes

  • Compute VaR and Expected Shortfall.
  • Stress-test portfolios.
  • Apply coherent risk measures.
  • Use copulas for dependence.
  • Discuss Basel III.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
QFN 506 Computational Finance +

Objective

To implement numerical algorithms for pricing.

Learning Outcomes

  • Implement Monte Carlo simulation.
  • Apply variance reduction.
  • Use finite difference for PDEs.
  • Apply trees for American options.
  • Profile quant code.
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
QFN 507 Algorithmic Trading +

Objective

To design systematic trading strategies.

Learning Outcomes

  • Apply mean-reversion and momentum.
  • Use statistical arbitrage.
  • Implement execution algorithms.
  • Apply market microstructure.
  • Backtest carefully.
QFN 508 High-Frequency Finance +

Objective

To analyse high-frequency financial data.

Learning Outcomes

  • Analyse market microstructure.
  • Apply order book models.
  • Use realised volatility estimators.
  • Apply jump-diffusion models.
  • Discuss latency arbitrage.
QFN 509 Machine Learning in Finance +

Objective

To apply ML to financial prediction.

Learning Outcomes

  • Apply supervised ML to returns.
  • Use deep learning for time series.
  • Apply RL to trading.
  • Address financial data idiosyncrasies.
  • Discuss model risk.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
QFN 510 Financial Econometrics +

Objective

To estimate econometric models on financial data.

Learning Outcomes

  • Apply ARMA and GARCH.
  • Use VAR models.
  • Apply cointegration.
  • Test unit roots.
  • Apply Markov-switching models.
QFN 511 Portfolio & Wealth Management +

Objective

To allocate capital across assets in client portfolios.

Learning Outcomes

  • Apply mean-variance optimisation.
  • Use Black-Litterman.
  • Apply risk parity.
  • Use factor models.
  • Build asset allocation models.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
QFN 512 Master's Dissertation +

Objective

To complete an original quant finance research project.

Learning Outcomes

  • Identify a research-quality problem.
  • Apply rigorous methodology.
  • Use real financial data.
  • Write a 15,000-word dissertation.
  • Defend orally.
Core Modules
💰

Fixed Income & Rates

Government bonds, swaps, caps, floors, swaptions, interest rate derivatives, and current SOFR/SONIA benchmark transition.

📈

Equity Derivatives

Vanilla and exotic equity options, variance swaps, volatility surface construction, and dividend modelling for equity products.

🤖

Machine Learning in Finance

Supervised and unsupervised learning, neural networks for alpha research, NLP for alternative data, and reinforcement learning for trading.

🔢

Econometric Modelling

Time series econometrics, factor model estimation, cointegration and pairs trading, and high-dimensional regression techniques.

📊

High-Frequency Trading Mathematics

Market microstructure theory, order book dynamics, optimal execution (Almgren-Chriss), market impact models, and latency arbitrage.

💼

Portfolio Management Theory

Factor investing, smart beta, risk parity, performance attribution, and alternative portfolio construction methodologies beyond mean-variance.

🧮

Volatility Modelling

Implied vol surface fitting, local-stochastic vol models, VIX products, volatility arbitrage strategies, and variance swap replication.

🏦

Risk Analytics & Regulatory Finance

Basel III/IV capital requirements, FRTB, IBOR transition, SA-CCR, and integrated risk management across trading and banking books.

Career Outcomes
🧮

Quantitative Researcher

Develop systematic investment signals and alpha generation strategies at quantitative hedge funds including Two Sigma, DE Shaw, and Man AHL.

💼

Portfolio Manager

Manage quantitative investment strategies across equities, fixed income, and alternatives with full profit and loss responsibility.

💰

Structured Finance Analyst

Structure, price, and distribute complex financial products including CLOs, CDOs, and structured notes for institutional clients.

🔢

Risk Strategist

Develop quantitative risk strategies, stress testing frameworks, and capital optimisation models within major financial groups.

📈

Volatility Trader

Trade equity and rates volatility products including variance swaps, VIX futures, and volatility surface arbitrage strategies.

🤖

FinTech Quant

Apply quantitative methods to build trading algorithms, robo-advisory models, and risk engines within leading fintech companies.

Top Universities for This Programme
University of Oxford LSE Imperial College London Bocconi University HEC Paris ETH Zurich NUS Singapore Columbia University University of Toronto University of Melbourne

Why D'Math University — Our 4-Step Approach

01

Quantitative Foundation

A pre-term bootcamp in stochastic calculus, Python, and econometrics ensures every student enters the taught programme at the same level of readiness.

02

Industry-Embedded Teaching

Partner firms contribute live case studies, real datasets, and practitioner-led workshops throughout all 12 taught modules — not just guest lectures.

03

Practitioner Capstone

The capstone project places students within a partner firm for 8 weeks, solving a real quantitative problem and presenting results to a professional panel.

04

Guaranteed Recruitment Access

Our 94% placement rate is backed by guaranteed interview slots at partner firms and a dedicated careers team supporting every student from Day 1.

Enrol in MSc Quantitative Finance →

Speak to an adviser — admissions@dmathu.ac | Quantitative readiness diagnostic included