D'Math University | Finance & Actuarial Mathematics

MSc Financial Mathematics

A rigorous postgraduate programme providing the mathematical treatment of modern finance. From stochastic calculus and Ito's Lemma to Monte Carlo simulation and exotic derivatives pricing — this programme is the gateway to elite quantitative finance careers in London, New York, and Zurich.

Postgraduate 1 Year Online Quant-Focused
10
Specialist Modules
£72k
Average Starting Salary
38+
Industry Partners
Wall St
& City Links
Programme Overview

The Mathematics of Modern Finance

The MSc Financial Mathematics immerses students in the mathematical theory underpinning derivatives pricing, risk management, and quantitative investment strategies. The programme begins with stochastic calculus and Ito's Lemma before progressing to Black-Scholes, interest rate models, credit risk, and numerical methods.

  • Stochastic Calculus: Brownian motion, Ito integrals, stochastic differential equations, and the Feynman-Kac formula.
  • Derivatives Pricing: Black-Scholes and extensions, local volatility, stochastic volatility (Heston), and jump-diffusion models.
  • Numerical Methods: Finite difference methods, Monte Carlo simulation, and tree methods for option pricing.
  • Dissertation: A substantial independent research project on a topic agreed with an academic supervisor and industry sponsor.

Programme Highlights

This programme attracts students with strong mathematics or physics backgrounds who seek entry into quantitative roles at investment banks, hedge funds, and proprietary trading firms. The curriculum is continuously updated in consultation with quant practitioners at leading firms.

  • Quant Lab: Hands-on sessions implementing pricing models in Python and C++ under the guidance of industry practitioners.
  • Guest Lecturers: Quantitative analysts from Goldman Sachs, Citadel, Deutsche Bank, and JP Morgan contribute to the programme.
  • Finance Library: Full access to QuantLib, Bloomberg, Refinitiv, and academic journal databases throughout the programme.
  • Interview Preparation: Dedicated sessions on quant interview brainteasers, probability puzzles, and technical case studies.
  • Placement Support: Guaranteed interview slots at 15+ partner investment banks and hedge funds for top-performing graduates.
Course Catalogue

Click any course to view its objective and learning outcomes.

FNM 501 Probability & Stochastic Processes +

Objective

To establish the probabilistic foundation for advanced finance.

Learning Outcomes

  • Apply measure-theoretic probability.
  • Use martingale theory.
  • Apply Brownian motion and Lévy processes.
  • Use Itô calculus rigorously.
  • Apply stochastic differential equations.
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
FNM 502 Stochastic Calculus +

Objective

To apply Itô calculus to derivative pricing.

Learning Outcomes

  • Apply Itô's lemma.
  • Solve stochastic differential equations.
  • Apply Girsanov's theorem for measure change.
  • 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
FNM 503 Derivatives Pricing +

Objective

To price exotic derivatives using arbitrage-free methods.

Learning Outcomes

  • Apply Black-Scholes-Merton framework.
  • Price barrier, Asian and lookback options.
  • Use risk-neutral valuation.
  • Apply local and stochastic volatility models.
  • 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%
FNM 504 Interest Rate Models +

Objective

To model the evolution of interest rates and price interest-rate derivatives.

Learning Outcomes

  • Apply Vasicek and CIR models.
  • Use HJM framework.
  • Apply LIBOR Market Model.
  • Price caps, floors and swaptions.
  • Calibrate to yield curves.
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
FNM 505 Credit Risk +

Objective

To model and price credit risk in fixed income.

Learning Outcomes

  • Apply structural and reduced-form models.
  • Price credit default swaps.
  • Use CDO pricing models.
  • 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
FNM 506 Numerical Methods for Finance +

Objective

To implement numerical algorithms for pricing and risk.

Learning Outcomes

  • Implement Monte Carlo simulation.
  • Apply variance-reduction techniques.
  • Use finite-difference methods for PDEs.
  • Apply tree methods for American options.
  • Profile and optimise 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.
FNM 507 Portfolio Theory +

Objective

To allocate capital across assets using modern portfolio theory.

Learning Outcomes

  • Apply mean-variance optimisation.
  • Use Black-Litterman framework.
  • Apply risk-parity allocation.
  • Use factor models.
  • Backtest allocation strategies.
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 — Portfolio Efficient Frontier
Two-asset portfolio. Adjust expected returns, volatilities, correlation and risk-free rate. The frontier (varying weights) is plotted; minimum-variance and tangency portfolios are highlighted.
μ₁ = 10.0% σ₁ = 15.0% μ₂ = 18.0% σ₂ = 30.0%
ρ = 0.20 rf = 3.0%
FNM 508 Risk Management +

Objective

To measure and manage risk across financial portfolios.

Learning Outcomes

  • Compute VaR via parametric, historical and Monte Carlo.
  • Apply Expected Shortfall.
  • Stress-test portfolios.
  • Apply coherent risk measures.
  • Discuss Basel III regulations.
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
FNM 509 Algorithmic Trading +

Objective

To design and backtest systematic trading strategies.

Learning Outcomes

  • Apply mean-reversion and momentum strategies.
  • Use statistical arbitrage.
  • Implement execution algorithms.
  • Apply market microstructure.
  • Backtest strategies properly.
FNM 510 Machine Learning in Finance +

Objective

To apply ML to financial prediction and trading.

Learning Outcomes

  • Apply supervised ML to return prediction.
  • Use deep learning for time series.
  • Apply reinforcement learning 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.
FNM 511 Quantitative Risk Modelling +

Objective

To build quantitative models of market and credit risk.

Learning Outcomes

  • Build internal models for capital.
  • Apply economic scenario generators.
  • Use copula methods for risk aggregation.
  • Apply extreme value theory.
  • Discuss model validation.
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.
FNM 512 Master's Dissertation +

Objective

To complete an original financial mathematics research project.

Learning Outcomes

  • Identify a research-quality problem.
  • Apply rigorous mathematical methods.
  • Use real financial data.
  • Write a 15,000-word dissertation.
  • Defend orally to a panel.
Core Modules
🧮

Stochastic Calculus & Ito's Lemma

Brownian motion, quadratic variation, Ito integrals, stochastic differential equations, and Girsanov's theorem for finance.

📈

Black-Scholes & Extensions

Black-Scholes derivation and PDE approach, local vol (Dupire), Heston stochastic vol, SABR, and jump-diffusion (Merton) models.

💰

Interest Rate Models

Short-rate models (Vasicek, CIR, Hull-White), HJM framework, LIBOR market model, and swap and cap/floor pricing.

🔢

Credit Risk Modelling

Structural models (Merton), reduced-form models, CDS pricing, CDO tranching, and counterparty credit risk (CVA).

📊

Monte Carlo Simulation

Variance reduction techniques, quasi-Monte Carlo, path-dependent option pricing, and simulation of correlated risk factors.

💼

Exotic Derivatives Pricing

Barrier, Asian, lookback, and digital options; structured products; and PDE and Monte Carlo pricing of complex payoffs.

🏦

Numerical Methods for Finance

Finite difference schemes (explicit, implicit, Crank-Nicolson), binomial and trinomial trees, and fast Fourier transform methods.

💻

Quantitative Programming

Implementing pricing models in Python (NumPy, SciPy, Pandas) and C++ for production-quality financial software development.

Career Outcomes
🧮

Quantitative Analyst (Quant)

Build and validate mathematical models for pricing, hedging, and risk management at investment banks, hedge funds, and asset managers.

📈

Derivatives Trader

Trade equity, rates, credit, and commodity derivatives on sell-side trading desks, market-making and managing option books globally.

🔢

Risk Manager

Develop and implement market risk frameworks, VaR and ES models, and stress testing programmes within major financial institutions.

💼

Structured Products Developer

Design and price complex structured products including notes, swaps, and hybrid derivatives for institutional and retail distribution.

🏦

Hedge Fund Analyst

Develop systematic and quantitative investment strategies using mathematical models at global macro, relative value, and quant hedge funds.

📊

Algorithmic Trader

Build, backtest, and deploy algorithmic trading systems using statistical models and machine learning for proprietary trading firms and HFT shops.

Top Universities for This Programme
University of Oxford Imperial College London Columbia University NYU Courant Institute University of Chicago ETH Zurich LSE University of Cambridge Princeton University Baruch College CUNY

Why D'Math University — Our 4-Step Approach

01

Mathematical Depth First

We begin with rigorous measure-theoretic probability and stochastic calculus so that every subsequent pricing model is built on unshakeable foundations — not approximations.

02

Practitioner-Led Teaching

Half of all lecture hours are delivered by current quantitative practitioners from investment banks and hedge funds, ensuring the curriculum reflects current market practice.

03

Implementation Focus

Every model studied is implemented in Python or C++ during lab sessions, building the programming competency that quant employers demand alongside mathematical ability.

04

Elite Placement Network

Our alumni hold senior quant positions at Goldman Sachs, Citadel, Two Sigma, JP Morgan, and the Bank of England — and actively recruit D'Math graduates each year.

Enrol in MSc Financial Mathematics →

Speak to an adviser — admissions@dmathu.ac | Quant readiness assessment available