BSc Financial Mathematics
A precisely crafted undergraduate programme that bridges pure mathematical theory with the financial models powering global capital markets. CFA-aligned curriculum with deep training in derivatives, portfolio theory, and financial modelling in Python and Excel.
Mathematics Meets Finance
The BSc Financial Mathematics develops rigorous mathematical skills alongside practical financial knowledge. Students graduate with the quantitative toolkit required for roles in investment banking, asset management, risk, and quantitative trading, with a curriculum explicitly aligned to the CFA Institute's investment analysis framework.
- Year 1: Mathematical foundations — real analysis, linear algebra, probability theory, and introductory economics.
- Year 2: Interest rate theory, options pricing, portfolio optimisation, financial econometrics, and risk measures.
- Year 3: Stochastic calculus introduction, fixed income mathematics, financial modelling, and dissertation project.
- CFA Alignment: Equity, fixed income, derivatives, and portfolio management modules map to CFA Level I & II syllabi.
Programme Highlights
This programme is designed for students who want the rigour of mathematics combined with the breadth and practical relevance of finance. It is the ideal launchpad for careers in investment banking, quantitative trading, or further postgraduate study in financial mathematics.
- Bloomberg Terminal Access: All Year 2 and 3 students have full Bloomberg terminal access for live market data analysis.
- Python & Excel Training: Dedicated financial modelling modules using Python (NumPy, pandas, scipy) and Excel VBA.
- Industry Mentors: Paired with professionals at Goldman Sachs, BlackRock, HSBC, and boutique asset managers.
- Summer Internship Support: Dedicated placement office with relationships at 40+ financial institutions in London and New York.
- CFA Scholarship: Top 10% of graduates receive a CFA Level I registration scholarship from D'Math University.
Click any course to view its objective and learning outcomes.
FNM 101 Calculus & Linear Algebra +
Objective
To revise and extend calculus and matrix algebra for finance applications.
Learning Outcomes
- Compute partial derivatives and Hessian matrices.
- Apply Lagrange multipliers in portfolio problems.
- Solve linear systems for hedging.
- Use eigendecomposition for risk attribution.
- Implement matrix routines in Python or R.
FNM 102 Probability for Finance +
Objective
To establish probability theory needed for asset pricing and risk modelling.
Learning Outcomes
- Apply conditional probability and the law of total expectation.
- Use moment generating functions for distributions of returns.
- Compute joint and marginal distributions of asset prices.
- Identify common heavy-tailed distributions in finance.
- Simulate random samples to estimate financial quantities.
FNM 103 Financial Mathematics I +
Objective
To value cash-flow streams under deterministic interest-rate assumptions.
Learning Outcomes
- Compute spot, forward and yield curves.
- Value annuities, perpetuities and amortising loans.
- Estimate Macaulay and modified duration.
- Construct cash-flow matched portfolios.
- Apply the term structure to bond pricing.
FNM 104 Stochastic Processes +
Objective
To model random evolution in continuous and discrete time for financial use.
Learning Outcomes
- Apply random walks and Markov chains to credit migration.
- Use Poisson processes to model arrivals.
- Simulate Brownian motion and geometric Brownian motion.
- Apply martingale theory to fair pricing.
- Compute first-passage probabilities.
FNM 201 Derivatives Pricing +
Objective
To price European, American and exotic derivatives using arbitrage-free methods.
Learning Outcomes
- Apply the Black-Scholes formula and its Greeks.
- Use binomial trees for American options.
- Price barrier and Asian options analytically and numerically.
- Discuss the limitations of constant-volatility models.
- Calibrate models to observed market prices.
FNM 202 Risk Management +
Objective
To measure and manage financial risk across portfolios.
Learning Outcomes
- Compute Value-at-Risk via parametric, historical and Monte Carlo methods.
- Apply Expected Shortfall and coherent risk measures.
- Stress-test portfolios under adverse scenarios.
- Manage credit, liquidity and operational risk.
- Discuss the regulatory landscape including Basel III.
FNM 203 Portfolio Theory +
Objective
To allocate capital optimally across assets given risk-return preferences.
Learning Outcomes
- Apply Markowitz mean-variance optimisation.
- Use the Capital Asset Pricing Model and its critiques.
- Compute Sharpe, Sortino and Information Ratios.
- Construct risk-parity and Black-Litterman portfolios.
- Backtest allocation strategies properly.
FNM 204 Time Series in Finance +
Objective
To model and forecast financial time series.
Learning Outcomes
- Fit ARIMA and GARCH models to returns.
- Test for stationarity and unit roots.
- Apply cointegration to pairs trading.
- Forecast volatility for option pricing.
- Detect and address structural breaks.
FNM 301 Computational Finance +
Objective
To implement numerical methods for pricing and risk in software.
Learning Outcomes
- Implement Monte Carlo simulation for path-dependent options.
- Apply variance-reduction techniques.
- Use finite-difference methods for PDE pricing.
- Build Greeks and sensitivities computationally.
- Profile and parallelise quant code.
FNM 302 Numerical Methods for PDEs +
Objective
To solve the parabolic PDEs that arise in derivative pricing.
Learning Outcomes
- Apply explicit, implicit and Crank-Nicolson schemes.
- Analyse stability and convergence.
- Solve Black-Scholes PDE with boundary conditions.
- Handle American exercise via PSOR.
- Compare PDE and Monte Carlo accuracy.
FNM 303 Insurance Mathematics +
Objective
To apply actuarial principles to life and general insurance products.
Learning Outcomes
- Construct life tables and price life insurance.
- Compute premiums and reserves for general insurance.
- Apply credibility theory in rating.
- Quantify risk transfer through reinsurance.
- Discuss solvency and capital regulations.
FNM 304 Financial Reporting & Modelling +
Objective
To translate quantitative results into auditable financial documents.
Learning Outcomes
- Build financial models in Excel and Python.
- Apply IFRS and accounting standards relevant to derivatives.
- Construct three-statement models for valuation.
- Document modelling assumptions for audit.
- Communicate model results to stakeholders.
Interest Rate Theory
Spot rates, forward rates, duration, convexity, yield curve construction, bootstrapping, and interest rate risk management.
Options & Derivatives
Put-call parity, binomial trees, Black-Scholes derivation, the Greeks, exotic options, and swap pricing fundamentals.
Probability for Finance
Measure theory foundations, random variables, characteristic functions, conditional expectation, and martingale basics.
Portfolio Optimisation
Markowitz mean-variance framework, efficient frontier, CAPM, factor models, Black-Litterman, and portfolio rebalancing strategies.
Financial Econometrics
ARIMA, GARCH, cointegration, VAR models, and regression analysis applied to financial time series in R and Python.
Risk Measures & VaR
Value at Risk, Expected Shortfall, coherent risk measures, copulas, and historical and Monte Carlo simulation approaches.
Fixed Income Mathematics
Government and corporate bond mathematics, credit spreads, duration hedging, immunisation strategies, and inflation-linked bonds.
Financial Modelling
Spreadsheet model construction in Excel VBA and Python for DCF analysis, option pricing, and portfolio performance attribution.
Financial Analyst
Analyse financial statements, build valuation models, and support investment decisions at banks, PE firms, and corporate finance boutiques.
Investment Analyst
Research equities, fixed income, and alternative investments for asset managers, hedge funds, and sovereign wealth funds globally.
Risk Analyst
Quantify market, credit, and liquidity risk within trading desks and risk management functions at major financial institutions.
Quantitative Trader
Develop and implement algorithmic trading strategies using mathematical models, statistical arbitrage, and quantitative signals.
Treasury Analyst
Manage cash, liquidity, FX exposure, and interest rate hedging within corporate treasury functions of multinational companies.
Fund Operations Analyst
Support portfolio accounting, NAV calculation, trade settlement, and performance reporting within fund administration and management firms.
Why D'Math University — Our 4-Step Approach
Rigorous Mathematical Core
We insist on genuine mathematical depth — students learn real analysis and measure theory before pricing derivatives, ensuring long-term professional versatility.
Applied Finance Integration
Every theoretical module is paired with a practical lab using real market data, Bloomberg, Python, or Excel, so theory and practice reinforce each other continuously.
CFA & Professional Prep
Dedicated CFA Level I preparation sessions in Year 3 help students sit professional exams before or immediately after graduation, accelerating their career trajectory.
Finance Career Launchpad
Our Finance Careers Hub connects students with internship and graduate role opportunities at 40+ partner banks, asset managers, and fintech firms from Year 1.
Speak to an adviser — admissions@dmathu.ac | CFA alignment review included