Introductory description

To provide PhD students in Finance and across the school with a sound understanding of
continuous-time finance and the econometric tools for conducting empirical research in
Finance and Economics.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Generalized Method of Moments (GMM) applications to asset pricing (with exposure to an econometric software)
2. Efficient frontier and the tangency portfolio; Mimicking portfolio analysis; Multivariate Sharpe ratios; Tests of asset-pricing models
3. Global minimum volatility portfolio; Tracking error volatility; Large scale covariance matrices; Out-of-sample returns; High frequency data; Principal components analysis
4. Model misspecification, model comparison, spurious factors in financial economics
5. Stochastic integrals, the Ito formula, stochastic differential equations
6. Measure changes, the Radon-Nikodym theorem, Girsanov's theorem, the martingale representation theorem, Feynman-Kac
7. Stochastic optimal control, optimal stopping theory. Dynamic programming in continuous time. Hamilton-Jacobi-Bellman equation
8. Applications to option pricing, real options, consumption-investment problems

Learning outcomes

By the end of the module, students should be able to:

• 1) Understand the properties of asset pricing models and master mean-variance analysis
• 2) Implement the associated estimation and testing procedures in the relevant software package
• 3) Use the virtual machines and scientific computing system for PhDs in Finance provided by WBS, including econometric software packages such as MatLab, R, or Python.
• 4) Understand and be able to implement in MATLAB, R, or Python the Generalized Method of Moments methodology as used in the mainstream Finance literature
• 5) Gain a solid understanding of the main mathematical tools in continuous-time finance
• 6)Be able to read and critically assess literature, formulate research questions and outline a basic structure in which to answer research questions

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Use the virtual machines and scientific computing system for PhDs in Finance provided by
WBS, including econometric software packages such as MatLab, R, or Python, and mathematical packages such as Mathematica

Transferable skills

Understanding of commonly used empirical and theoretical techniques in Finance.
Ability to analyse data using some of these techniques as implemented in a software of your choice