Dr Domingos Romualdo

This course is available on the Global MSc in Management, MSc in Accounting and Finance, MSc in Applicable Mathematics, MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Quantitative Methods for Risk Management, MSc in Risk and Finance, MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). This course is not available as an outside option.

Global MSc in Management ('Accounting and Finance' and 'Finance' concentrations only).

This course is available to other students from the Departments of Economics, Mathematics, and Statistics where regulations permit. This course is not available as an outside option.

A strong background in statistics and quantitative methods at the undergraduate level is required.  Prior programming experience is helpful.  Students without prior knowledge of Matlab are encouraged to take FM457A (Computational Tools in Finance) concurrently.

This graduate-level course covers important quantitative and statistical tools in applied finance. It studies financial markets risk, with a particular focus on models for measuring, assessing and managing financial risk.  Students will be introduced to the application of these tools and the key properties of financial data through a set of computer-based homework assignments and classes.

The course aims to introduce quantitative concepts and techniques in many areas of finance.   Sample topics include Risk Measures (e.g., Value-at-Risk and Expected Shortfall, including implementation and backtesting), univariate and multivariate volatility models, Factor Models, Principal Components Analysis, Options Pricing, Binomial Trees, Monte Carlo Simulations, and associated topics in Econometrics.  This list is meant to be representative, but topics may be added or removed.

Implementing the models and tools in MATLAB is an essential part of the course.  The homework assignments are designed to guide the students to all stages of the analytical process, from locating, downloading and processing financial data to the implementation of the tools and interpretation of results. Students will have the opportunity to explore the databases available at the LSE and to become comfortable working with real data.

20 hours of lectures and 10 hours of seminars in the MT.

Six homework assignments to be solved using MATLAB.

No single text covers the course material.  The relevant sections of the following readings would be appropriate for individual topics: Jon Danielsson (2011), Financial Risk Forecasting; Ruey Tsay (2010), Analysis of Financial Time Series; Pietro Veronesi (2010), Fixed Income Securities: Valuation, Risk, and Risk Management.

Exam (75%, duration: 1 hour and 30 minutes, reading time: 10 minutes) in the summer exam period.
Project (20%, 2000 words) and presentation (5%) in the MT.