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Financial Modelling and Optimization (MSc/PgDip)

Course Description

Programme description

This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets.

We will give you sound knowledge in financial derivative pricing, portfolio optimization and financial risk management.

We will also provide you with the skills to solve some of today’s financial problems, which have themselves been caused by modern financial instruments. This expertise includes modern probability theory, applied statistics, stochastic analysis and optimization.

Adding depth to your learning, our work placement programme puts you at the heart of financial organisations such as Aberdeen Asset Management, Barrie & Hibbert and Lloyds Banking Group.

Programme structure

This programme involves two taught semesters of compulsory and option courses, followed by a dissertation project.

Compulsory courses:

Discrete-Time Finance
Finance, Risk and Uncertainty
Fundamentals of Optimization
Optimization Methods in Finance
Research-Linked Topics
Risk-Neutral Asset Pricing
Stochastic Analysis in Finance I
Stochastic Analysis in Finance II

Option courses:

Advanced Time Series Econometrics
Combinatorial Optimization
Credit Scoring
Fundamentals of Operational Research
Financial Risk Management
Computing for Operational Research and Finance
Large Scale Optimization for Data Science
Microeconomics 2
Nonlinear Optimization
Numerical Partial Differential Equations
Parallel Numerical Algorithms
Programming Skills
Risk Analysis
Stochastic Modelling
Stochastic Optimization

Career opportunities

Graduates have gone on to work in major financial institutions or to continue their studies by joining PhD programmes.

Visit the Financial Modelling and Optimization (MSc/PgDip) page on the University of Edinburgh website for more details!

Entry Requirements

The minimum entry requirement is a UK 2:1 degree, or its international equivalent, in mathematics or a mathematical subject such as statistics, physics or engineering.

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Recipient: University of Edinburgh

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