Aims

This module aims to provide students with a detailed understanding of the key mathematical and statistical techniques used by financial institutions (e.g. investment banks, insurance companies, hedge funds) for modelling the risks associated with their assets and liabilities. The module will also explain in detail how financial regulators influence the risk management strategies employed by financial institutions.

Outline Of Syllabus

This module concentrates on the techniques used for modelling three particular types of risk;
(i) interest rate risk
(ii) credit risk
(iii) market risk.

These techniques range from simple mathematical models, through to more complex statistical techniques such as Monte Carlo simulation. Empirical applications at the individual, company and country level will be employed to illustrate the techniques in action. Relevant financial regulations will also be critically analysed. A summary of the lecture schedule is given below.

1. Introduction and interest rate risk
- types of risk, the repricing model, the maturity model.
2. Interest rate risk
- duration, immunization, convexity.
3. Interest rate risk and credit risk
- forecasting interest rates, linear discriminant models, linear probability models.
4. Credit risk
- linear probability models, limited dependent variable models.
5. Credit risk and market risk
- default rate models, modelling and forecasting volatility.
6. Market risk
- VaR by historical simulation, VaR by the delta-normal approach, VaR by Monte Carlo simulation, backtesting VaR models.
7. Market risk
- backtesting VaR models, VaR and financial regulation, VaR and the 2008-2009 global financial crisis.

Teaching Rationale And Relationship

• Lectures provide the basic structure of the methods and theories that are introduced and an overview of the current issues.
• Seminars provide an opportunity to enhance understanding of the empirical methods and the theoretical aspects of the module.

Assessment Rationale And Relationship

The examination tests understanding of how econometrics can be used to model credit risk.