Computational Finance - EC845

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury Spring
View Timetable
7 15 (7.5)


EC824 - Advanced Topics in Financial Economics





The aim of this module is to offer hands-on training in computational finance. Given proliferation of new financial products, finding their theoretical prices are now routine in financial industry. Hence, just knowing the theoretical foundations is, although indispensable, not enough for the students who seek their career as financial professionals. The module discusses two lines of computation ideas. The first approach is Martingale asset pricing, in which the students are expected to perform Monte Carlo simulations and use tree models to compute the theoretical prices of a wide range of financial derivatives. The second technique is finite difference methods to solve the Hamilton-Jacobi-Bellman pricing equations numerically. Both computational approaches are the acknowledged standards in a variety of modern quantitative finance suites used worldwide. The module starts with the theoretical foundations of each line of computation ideas and a short introduction to programming.


Contact hours

32 hours of academic teaching in the form of lectures and seminars

Method of assessment

50% Technical Notes (4,000 words)
50% Take-Home Exam

Indicative reading

• Björk, Thomas. Arbitrage theory in continuous time. 3rd Edition. Oxford University Press, 2009
• Evans, Gwynne, Blackledge, Jonathan and Peter Yardley. Numerical methods for partial differential equations. Springer, 2000
• Evans, Gwynne, Blackledge, Jonathan and Peter Yardley. Analytic methods for partial differential equations. Springer, 1999
• Judd. Numerical Methods in Economics. MIT press, 1998.
• Pliska, Stanley. Introduction to Mathematical Finance: Discrete Time Models. Blackwell, 1997
•Wilmott, Paul, Howison, Sam and Jeff Dewynne. The mathematics of financial derivatives: a student introduction. Cambridge University Press, 1995

See the library reading list for this module (Canterbury)

Learning outcomes

On successfully completing the module students will be able to:

• comprehensively understand martingale measure theory and dynamic optimization theory
• critically understand and systematically apply Monte Carlo Method and Feynman-Kac's stochastic representation to quantify martingale measure problems
• critically understand and systematically apply Tree Model and Finite Difference Method to quantify dynamic programming solutions of investor's optimal choice
• flexibly use advanced concepts in dynamic optimization, stochastic calculus, etc. to understand the real world problems
• write computer programmes for advanced computational methods
• analyse complex financial data at a high level of generality

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