Dr Jon Danielsson

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 Finance and Risk, MSc in Financial Mathematics, MSc in Quantitative Methods for Risk Management, 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 capped, any eligible student that requests a place will be given one.

This course does not permit auditing students. 

A strong background in statistics and quantitative methods at the undergraduate level is required.  Prior programming experience is helpful.  

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, Monte Carlo Simulations, and associated topics in Econometrics.  This list is meant to be representative, but topics may be added or removed. Recent stress events, such as the global crisis in 2008, Covid-19 in 2020, Russia’s invasion of Ukraine and recent inflation are used to illustrate the various methodologies presented in the course.

An essential part of the course is implementing the models and tools in the programming language R. Further information on R as used in the course can be found in the R Notebook at  https://www.financialriskforecasting.com/notebook.

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 AT.

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; The lecture slides and supporting programming material can be found on www.financialriskforecasting.com/

Other background reading is Ruey Tsay  (2010), Analysis of Financial Time Series; Peter Christoffersen (2003) Elements of Financial Risk Management; Alexander J. McNeil, Rüdiger Frey, et al. , (2015) Quantitative Risk Management: Concepts, Techniques and Tools.

Exam (30%, duration: 1 hour and 30 minutes, reading time: 10 minutes) in the spring exam period.
Continuous assessment (70%) in the AT.