Module manager: Prof Nick Wilson
Email: n.wilson@lubs.leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2017/18
| LUBS2570 | Introduction to Econometrics |
| LUBS2670 | Statistics for Business and Economics 2 |
This module is not approved as a discovery module
The aims of this module are to give students: a knowledge of research methods and statistical financial analytical techniques and how they can be used to analyse complex financial data sets, credit decisions and in credit risk management (credit scoring methods); the opportunity to develop their knowledge and understanding of the practical application of credit and financial analytics in practice and in research in credit and finance, and an appreciation of some of the current research being conducted in credit risk management and finance.
On completion of the module students will be able to:
- Identify and use modern credit and financial analytical techniques as applied in research in finance and in practice in the financial services within the Basel II/III regulatory framework;
- Critically evaluate analytical tools used in credit risk modelling and develop the quantitative skills required to assess and manage default risk and the associated metrics used by banks and other lenders;
- Apply analytical techniques to model, analyse and predict financial market and individual/corporate behaviour and test theories and hypothesise in the discipline.
Transferable skills:
- Written communication skills
- Critical thinking skills
- Advanced numeracy skills
Subject specific skills:
On completion of this module students will be able to:
Demonstrate advanced problem solving, analytical and quantitative skills by applying current theory and appropriate analytical tools to complex problems in credit risk management.
Indicative content:
- Introduction to Econometrics and Applications in Finance
- Statistical Foundations, Descriptive Statistics, Distributions and Hypotheses Tests Data Types and Issues, Topics and Concepts in Finance.
- Correlation and Simple Regression. Least Squares Methods and Diagnostics. Testing. Hypothesis Testing. Issues Relating to Autocorrelation.
- Multiple Regression. Specification and Testing of Regression Models. Functional Forms and Transformations. Dummy Variables. Issues Relating to Multi-collinearity, autocorrelation and heterscedasticity. Applications and Examples.
- Analysis of Limited Dependent Variables. Probability. Linear probability and Logistic Regression. Models, Tests and Interpretation. Applications in Finance: Credit Risk and Probability of Default. Other Limited Dependent Variables.
- Elementary time series estimation.
- Introduction to credit scoring techniques in retail and corporate lending. Estimation and management of credit risk components: PD (probability of default), LGD (loss given default), and EAD (exposure at default). Assessing and validating credit risk models.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 20 | 2 | 40 |
| Seminar | 16 | 1 | 16 |
| Private study hours | 144 | ||
| Total Contact hours | 56 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Students are required to undertake specified pre-reading and question preparation in advance of the classes based on the material introduced in the lectures. This includes some online pre-reading and formative questions.
Students will be able to monitor their progress through fortnightly seminars. Model answers and answers and marking schemes for workshop papers against which students can assess their own performance;
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 2.0 Hrs Mins | 50 |
| Standard exam (closed essays, MCQs etc) (S1) | 2.0 Hrs Mins | 50 |
| Total percentage (Assessment Exams) | 100 | |
The resit for this module will be 100% by examination.
The reading list is available from the Library website
Last updated: 25/01/2018
Errors, omissions, failed links etc should be notified to the Catalogue Team