Students on the MPhil in Finance programme choose 3 electives from a number of modules offered by Cambridge Judge Business School, the Faculty of Economics, and the Faculty of Mathematics.
The electives offered may vary from year to year. The list below should therefore be regarded as illustrative:
The How to Do Finance course introduces you to the methods, approaches, and strategies of research through a few recent and promising research topics in financial economics, providing a broad overview of the key theoretical and empirical methodological issues in each topic, as well as details related to the practical skills of organising, structuring, writing, and publishing research. The course is run by the consortium of Finance faculty members of the Cambridge Judge Business School. Experienced faculty members also share their insights on their current research and research papers.
Taught by the Faculty of Mathematics (part of the Maths Part III degree).
This module is an introduction to financial mathematics, with a focus on the pricing and hedging of contingent claims. It complements the material in Advanced Probability and Stochastic Calculus & Applications.
The course covers a selection of topics including:
- Discrete-time models. Arbitrage, martingale deflators, the fundamental theorem of asset pricing. Numeraires, equivalent martingale measures. Forwards, options, futures, bonds, interest rates. Attainable claims, market completeness. The Breeden-Litzenberger formula. Fourier pricing. American claims.
- Continuous-time models. Admissible strategies. Absolute and relative arbitrage. Existence of replicating strategies. Pricing and hedging via partial differential equations. The implied volatility surface. Dupire’s formula. Stochastic volatility models. The HJM approach to term structure. Merton’s problem.
Taught by the Faculty of Mathematics.
The Advanced Probability course introduces you to advanced topics in modern probability theory. The emphasis is on tools required in the rigorous analysis of stochastic processes, such as Brownian motion, and in applications where probability theory plays an important role.
A basic familiarity with measure theory and the measure-theoretic formulation of probability theory is very helpful. These foundational topics are at the beginning of the course, but student unfamiliar with them are expected to consult the literature to strengthen their understanding (for instance, Probability with Martingales by D. Williams, 1991).
Taught by the Faculty of Economics.
This course offers an introduction to the behavioural approach to economics. Among the topic covered are behavioural game theory, intertemporal decision making, neuroeconomics, cognitive biases, decision-making heuristics and addiction. The course includes both theoretical and empirical material, but a recurring theme is the importance of experimental findings both in the laboratory and in the field.
The Continuous Time Finance course provides you with an overview of continuous-time finance methods and their applications to corporate finance and financial economics.
The course is taught primarily on the basis of journal articles, supplemented with the lecturer’s own teaching notes. Throughout the course you should also learn critically to assess and evaluate papers.
Important note: This course is offered biennially. If you wish to continue onto the PhD at Cambridge Judge, this course is mandatory if it’s running during your MPhil year.
Taught by the Faculty of Economics.
The course introduces you to the economics of networks. This area of research has emerged in the last 2 decades and it has introduced a set of tools for economists to incorporate network structure in the analysis of individual behaviour and economic outcomes.
Topics covered include the formation of networks, the provision of local public goods, coordination, learning, trading, and financial networks. A central focus of the course is the interplay between theory and experiments.
In a large number of empirical contexts in finance and management, data are temporarily ordered in the form of time series. The Time Series Econometrics module introduces you to concepts and methods that are appropriate for empirical research in such settings, covering methods for exploratory time series analysis, estimation of dynamic causal effects and forecasting.
Taught by the Faculty of Economics.
The Industrial Organisation course provides a rigorous treatment of the main concepts in industrial organisation. The course covers both theory and applications.
Taught by the Faculty of Economics.
This course is on development economics and deals with the economic problems of poor countries. It considers some of the main theoretical and analytical issues in development economics as well as the historical development process of now-developed countries. The topics covered are growth, development, poverty, inequality, education, technology, innovation, mutual insurance, finance, savings, weather, climate, health, pandemics, representative democracy, religion, social capital and conflict.
Taught by the Faculty of Mathematics (part of the Maths Part III degree).
The goal of this module is to present and analyse efficient numerical methods for ordinary and partial differential equations. The exposition is based on few basic ideas from approximation theory, complex analysis, theory of differential equations and linear algebra, leading in a natural way to a wide range of numerical methods and computational strategies. The emphasis is on algorithms and their mathematical analysis, rather than on applications.
The module consists of 3 parts: methods for ordinary differential equations (with an emphasis on initial-value problems and a thorough treatment of stiff equations), numerical schemes for partial differential equations (both boundary and initial-boundary value problems, featuring finite differences and, time allowing, finite element methods) and numerical algebra of sparse systems (inclusive of fast Poisson solvers, sparse Gaussian elimination and iterative methods). We start from the very basics, analysing approximation of differential operators in a finite-dimensional framework, and proceed to the design of state-of-the-art numerical algorithms.
Taught by the Faculty of Mathematics.
This course introduces you to Itô calculus.
- Brownian motion. Existence and sample path properties.
- Stochastic calculus for continuous processes. Martingales, local martingales, semi-martingales, quadratic variation and cross-variation, Itô’s isometry, definition of the stochastic integral, Kunita-Watanabe theorem, and Itô’s formula.
- Applications to Brownian motion and martingales. Levy characterization of Brownian motion, Dubins-Schwartz theorem, martingale representation, Girsanov theorem, conformal invariance of planar Brownian motion, and Dirichlet problems.
- Stochastic differential equations. Strong and weak solutions, notions of existence and uniqueness, Yamada-Watanabe theorem, strong Markov property, and relation to second order partial differential equations.
- Stroock-Varadhan theory. Diffusions, martingale problems, equivalence with SDEs, approximations of diffusions by Markov chains.
Prerequisites: We assume knowledge of measure theoretic probability as taught in Part III Advanced Probability. In particular we assume familiarity with discrete-time martingales and Brownian motion.
This course offers training in the basic techniques of financial accounting and then introduces you to select applied settings that use financial reporting information for valuation and investment decisions.
The course first focuses on building a foundation of knowledge for understanding accounting measurement and reporting. Accounting is in essence a model for recording and presenting economic information, and the starting point is to grasp how this model works and understanding its strengths and limitations.
The second part of the course focuses on applying accounting knowledge to assess and evaluate companies through case studies.
The following 2 modules can also be taken as electives, provided you’ve not already taken them as a core course:
This course builds upon Asset Pricing I. Asset Pricing II examines how assets are priced in practice and how pricing models are implemented via basic trading strategies.
The course is delivered using lecture slides, Jupyter notebooks, and a free online platform for building and backtesting algorithmic strategies (Quantopian). Some knowledge of Python would be helpful (although not a prerequisite).
Topics covered include:
- Asset pricing: foundations and quantitative implementation
- Valuing risk
- Estimation techniques and model selection
- Hypothesis testing
- Small sample inference
- Modelling, estimating and forecasting volatility
- Introduction to machine learning
This course follows on Corporate Finance I that is taught in Michaelmas term. It introduces students who would like an academic career to fundamental empirical research in corporate finance.
Important to note: This course is heavily research-based. It is not suited and not recommended for you if you wish to enter a non-academic career. It has a very heavy reading load of original research papers that you’re required to be familiar with before you enter the class. There is no textbook for the course.
By the end of the course, you should be able to:
- appreciate the theoretical foundations of and empirical evidence on capital raising, internal capital market and control, and corporate governance
- assess critically the theoretical and empirical debates in the corporate finance literature
Please note that if you’re planning on continuing on to a PhD at the School, you will need to choose particular electives.