Mathematical Finance MSc
Master
In Birmingham
Description
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Type
Master
-
Location
Birmingham
This programme, taught jointly by the School of Mathematics and the Department of Economics, provides the skills that will enable technically able graduates (including in mathematics, science and engineering) to apply their quantitative training to financial analysis.
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Subjects
- GCSE Mathematics
- Programming
- Financial Training
- IT risk
- University
- Financial
- Finance
- Simulation
- Economics
- Mathematics
- Algorithms
- Options
- Risk
Course programme
The programme comprises 180 credits in total (credits are given in brackets).
Term 1 (October – December) Core Modules- Econometrics with Financial Applications (15 - Term 1)
forecasting; stochastic volatility; ARCH; GARCH; co-integration; statistical-arbitrage; non-stationarity; unit roots - Introduction to Quantitive Finance (10)
options pricing; Black-Scholes; European and American options; exotic options; fixed income; binomial method; random walks - C++ for Finance (10 - Term 1)
valuation system, simulation, polymorphic factory, design patterns, Boost library - Computational Methods and Programming (20)
- Numerical Methods II (10)
Interpolation methods (including piecewise polynomial), numerical integration (including Newton-Cotes and Gaussian quadrature), finite difference method for boundary-value problems, convergence acceleration and Richardson extrapolation.
- Risk Analytics* (10)
copulas; Value-at-Risk; expected shortfall (cVaR); mean-variance portfolio optimization; PCA; stress testing; Black-Litterman; live trading
* Alternatively, students can attend the Advanced Risk and Portfolio Management Bootcamp in advance.
Optional Modules- International Banking and Finance (20)
- Macroeconomics (20)
Economic growth, consumption, investment, exchange rates, interest parity conditions, overshooting, speculative attacks, inflation, monetary policy. - Nonlinear Programming I (10)
Optimality condition; convex set and convex function; duality theory; unconstrained optimization; constrained optimization; conjugate gradient algorithms; Newton-type algorithms; interior point algorithms; Lagrangian methods. - Topics in Money and Banking (10)
- Integer Programming (10)
Alternative formulations; optimality; relaxation; primal and dual bounds; total unimodularity; cut-plane algorithm; branch and bound method; network flow problems; knapsack problems; matching problem; assignment problem; set covering problem - Game Theory (10)
- Conic Optimization (10)
Relevant modules for those without all the requisite undergraduate mathematics training include: PDEs, Transform Theory, and Complex Variable Theory for Physicists. Graduate modules offered elsewhere in the University may also be taken with the Programme Director's approval.
Term 2 (January - March) Core Modules- Econometrics with Financial Applications (15 - Term 2)
forecasting; stochastic volatility; ARCH; GARCH; co-integration; statistical-arbitrage; non-stationarity; unit roots - Exotic options, bonds and further quantitative finance (10)
options pricing; Black-Scholes; European and American options; exotic options; fixed income; binomial method; random walks - C++ for Finance (10 - Term 2)
valuation system, simulation, polymorphic factory, design patterns, Boost library - Numerical Linear Algebra with Applications (10)
iterative methods for sparse linear systems, numerical methods for eigenvalue problems, FEM matrix analysis, fast Poisson solvers, eigenvalue computation for elliptic problems.
- Non-Linear Programming II (10)
Optimality condition; convex set and convex function; duality theory; unconstrained optimization; constrained optimization; conjugate gradient algorithms; Newton-type algorithms; interior point algorithms; Lagrangian methods. - Combinatorial Optimisation (10)
Alternative formulations; optimality; relaxation; primal and dual bounds; total unimodularity; cut-plane algorithm; branch and bound method; network flow problems; knapsack problems; matching problem; assignment problem; set covering problem - Advanced quantitative finance: crashes, volatility, multiple assets and hedging (10)
crashes; volatility modeling; multi-asset options; hedging; liquidity; asset allocation; stochastic control; historical lessons; Monte Carlo - Heuristic Optimisation (10)
Exhaustive search; tapu-search, local search; greedy algorithms; dynamic programming; computer simulation; evolutionary Algorithms. - Experimental and Behavioural Economics (10)
- Further Mathematical Finance (10)
- Topics in Management Mathematics (10)
Relevant modules for those without all the requisite undergraduate mathematics training include: Numerical Methods in Linear Algebra, Programming. Graduate modules offered elsewhere in the University may also be taken with the Programme Director's approval.
Term 3 (May - June)Examination Period
July - September
Dissertation (40)
Students are encouraged to pursue internships while writing their dissertations.
The optional modules listed on the website for this programme may unfortunately occasionally be subject to change. As you will appreciate key members of staff may leave the University and this necessitates a review of the modules that are offered. Where the module is no longer available we will let you know as soon as we can and help you make other choices.
Mathematical Finance MSc