MSc Financial Modelling and Optimization

1 Year On Campus Masters Program

University of Edinburgh

Program Overview

The MSc Financial Modelling and Optimization equips you with advanced mathematical, statistical, and computational tools to analyse, model and solve complex problems in finance and beyond — from derivative pricing to risk management and portfolio optimisation. It’s ideal if you have strong quantitative or mathematical skills (or a background in maths, statistics, engineering, physics, etc.) and you want a rigorous master’s that positions you for high-demand roles in quantitative finance, risk analytics, energy markets, or further research.

Curriculum Structure

This is a full-time, 1-year (or part-time over 2 years) master’s. You complete taught coursework over two semesters, followed by a dissertation project in the summer.

In Semester 1, compulsory courses include Discrete-Time Finance, Fundamentals of Optimization, and Stochastic Analysis in Finance — giving you grounding in mathematical finance theory, probability and stochastic processes, and optimisation techniques essential for pricing and risk modelling. These modules strengthen your toolkit in advanced maths and finance theory from day one.

In Semester 2, compulsory modules such as Numerical Probability and Monte Carlo, Optimization Methods in Finance, Risk-Neutral Asset Pricing, and Research Skills for Financial Mathematics take you deeper into computational finance, probabilistic simulation, asset pricing under uncertainty, and applied methods for real-world financial problems. This phase helps bridge theory and practice, training you to implement models and perform rigorous financial analysis.

Alongside these core courses, you also have a range of optional modules — for example Stochastic Modelling, Finance, Risk and Uncertainty, Operational Research fundamentals, Machine Learning / Data-Science oriented courses, Stochastic Control and Dynamic Asset Allocation, and others — giving you flexibility to shape the degree toward quantitative finance, risk analytics, data science, or even energy-market modelling depending on your interest.

Finally, in the summer term, you complete a dissertation project (60-credit). This project lets you apply your mathematical and computational skills to a real-world or research-level problem — it can be academic, or even linked to an industry partner (for example banks, financial institutions, or energy-sector firms), making it a strong demonstration of your capability to future employers or PhD admissions panels. School of Mathematics+2Study at Edinburgh+2

Focus Areas

Financial mathematics; derivative pricing; portfolio and risk optimisation; stochastic analysis; computational finance; numerical probability & Monte Carlo simulation; mathematical and statistical methods; optional specialisations in data science, risk theory, allocation modelling, or operational research.

Learning Outcomes

You will master mathematical and computational tools used in modern finance, develop the ability to price complex financial instruments and assess risk, gain strong skills in portfolio optimisation and numerical simulation, and build competence to model and solve real-world financial and economic problems. You will also build transferable skills — logical reasoning, quantitative problem-solving, programming or computational skills — which are valuable in finance, energy markets, data analytics, and more.

Professional Alignment (Accreditation & Relevance)

Offered by the University’s School of Mathematics, the programme is structured around the demands of employers who need strong quantitative and computational finance expertise — for roles in banks, financial institutions, risk management teams, trading desks, energy-sector finance, or data-driven financial analysis. It serves as a strong foundation for roles in quantitative finance, risk analytics, energy economics, or for further academic research (e.g. PhD).

Reputation (Employability & Graduate Outcomes)

Graduates of this MSc have moved into major financial institutions or continued into PhD-level research. The programme has strong industry links and offers opportunities for dissertation projects with external partners, which helps build real-world experience and professional networks.

Experiential Learning (Research, Projects, Internships etc.)

Students develop real analytical and modelling skills from day one. The programme integrates mathematical modelling, numerical optimization, stochastic processes, and algorithm design using professional software used in financial institutions. You’ll work in specialist labs, collaborate with peers on quantitative group projects, and apply high-level mathematics to real financial scenarios:

Dedicated computational labs within the School of Mathematics, equipped for financial modelling, numerical optimization, algorithmic testing, and scenario simulation.
Extensive use of core software such as MATLAB, Python, R, and optimization packages, allowing students to build financial algorithms, simulate markets, and perform large-scale numerical analysis.
Group modelling projects, where students collaboratively develop financial optimization models, test them with real datasets, and present findings — mirroring professional quantitative teams.
Advanced mathematical and optimization tools integrated into coursework, such as linear and nonlinear optimization solvers, stochastic modelling tools, and numerical algorithms.
Supervised dissertation research, allowing students to work directly with academic researchers on topics like portfolio optimization, stochastic control, risk modelling, or algorithmic trading.
Access to the University of Edinburgh’s world-class library network, including digital research databases, journals, financial datasets, and mathematics/finance resources.
Opportunities to engage with the Edinburgh Mathematical Finance Group, a research-active environment where students are exposed to seminars, projects, and mathematical finance innovations.
Workshops and training sessions hosted by the School of Mathematics, teaching computational modelling, advanced programming, numerical optimization methods, and research techniques.
Specialist teaching rooms and study spaces, equipped for mathematical modelling tutorials, problem-solving sessions, and collaborative quantitative work.

Progression & Future Opportunities

Graduates of the MSc Financial Modelling and Optimization at the University of Edinburgh step into highly analytical and well-paid career paths. Typical outcomes include roles such as Quantitative Analyst, Data Scientist in Finance, Risk Modelling Specialist, Financial Engineer, or Investment Analytics Associate. This programme is ideal for students who want to blend finance with mathematical modelling, machine learning, and optimization techniques — a skillset in extremely high demand across the global financial sector.

Career Prospects & Why This Programme Is a Smart Investment:

University support that boosts your employability
- Edinburgh offers one-to-one career coaching, application guidance, technical interview prep, and employer networking events specifically geared toward quantitative finance and analytics roles.
- Students gain access to industry speakers, employer sessions, and alumni working in quantitative roles, helping you understand job expectations and build technical confidence.
Strong global demand for modelling and optimization skills
- Banks, hedge funds, investment firms, and fintech companies are actively seeking graduates who can build financial models, optimise portfolios, price derivatives, and apply machine learning to financial data.
- Employers favour this degree because it develops strong skills in optimization algorithms, stochastic modelling, programming, numerical methods, and computational finance — core tools used in quant and modelling jobs.
Industry-relevant curriculum & advanced technical training
- The programme covers areas such as stochastic processes, optimization techniques, computational methods, numerical analysis, and statistical modelling — giving you the technical depth required for high-skill roles.
- You’ll work on real modelling problems and research-driven projects where you apply mathematical and computational techniques to financial scenarios, building a strong portfolio of work for employers.
High academic credibility & long-term value
- The University of Edinburgh is globally recognised for excellence in mathematics, informatics, and finance — giving long-term credibility to your degree.
- The analytical and computational skills developed here are future-proof: they are used in quantitative finance, risk analytics, machine-learning-driven trading, fintech modelling, and operations research.

Further Academic Progression:
After completing the MSc, you could:

Pursue a PhD in Quantitative Finance, Financial Engineering, Applied Mathematics, Optimization, or Computational Modelling, which opens doors to academic, research, or specialist quant roles.
Enhance your professional profile by pursuing CFA, FRM, CQF, or advanced machine-learning certifications, depending on your career direction.
Transition to research or technical teams in financial institutions, trading firms, or analytics-focused organisations, and later specialise in areas like algorithmic trading, stochastic modelling, or optimization research.