Course summary

The information provided on this page was correct at the time of publication (November 2023). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas. The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of problems. You will develop skills to formulate mathematical problems that are based on the needs of the financial industry. You will carry out relevant mathematical and financial analysis, develop and implement appropriate tools to present and interpret model results. The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution. You will take four introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics, financial markets and instruments, and Python. The first term focuses on compulsory core material, offering 64 hours of lectures and 24 hours of classes, plus one compulsory computing course offering 16 hours of lectures. Core courses

• Stochastic Calculus (16 lectures, and 4 classes of 1.5 hours each)

• Financial Derivatives (16 lectures, and 4 classes of 1.5 hours each)

• Numerical Methods (16 lectures, and 4 classes of 1.5 hours each)

• Statistics and Financial Data Analysis (16 lectures, and 4 classes of 1.5 hours each)

• Financial computing with C++ I (16 hours of lectures, plus 4 classes of 2 hours each over weeks 1-9)

• Deep Learning (16 lectures, and 4 classes of 1.5 hours each)

• Quantitative Risk Management (8 lectures, and 2 classes of 1.5 hours each)

• Stochastic Control (8 lectures, and 2 classes of 1.5 hours each)

• Fixed Income (16 lectures, and 4 classes of 1.5 hours each)

• Advanced Volatility Modelling (8 lectures, and 2 classes of 1.5 hours each)

• Advanced Monte Carlo Methods (8 lectures, and 2 classes of 1.5 hours each)

• Advanced Numerical Methods (8 lectures, and 2 classes of 1.5 hours each)

• Asset Pricing (8 lectures, and 2 classes of 1.5 hours each)

• Market Microstructure and Algorithmic Trading (8 lectures, and 2 classes of 1.5 hours each)

• Decentralised Finance (8 lectures and 2 classes of 1.5 hours each)

• Financial computing with C++ II (24 hours of lectures and classes)