2022/23 Taught Postgraduate Module Catalogue

LUBS5025M Financial Modelling and Analysis

15 creditsClass Size: 100

Module manager: Ali Altanlar
Email: A.Altanlar@lubs.leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2022/23

This module is not approved as an Elective

Module summary

The module equips you with a basic through to more advanced level of understanding of the techniques of modern econometric practices as applied in finance and economic research. These techniques include data manipulation, knowledge of available financial databases; multivariate regression techniques and panel data estimation; time series modelling and analyses of limited dependent variables. Appropriate analytical tools (econometric methods and the use of appropriate econometric software) required to model, analyse and predict financial market and corporate behaviour and to test theories and hypothesis in this field are developed. The module provides technical skills necessary to undertake empirical research. The course will be taught, as far as possible, in a non- technical way (without matrix algebra), with the emphasis on applications and practice. You will be given computer-based assignments in addition to class assignments.

Objectives

The aim of this module is to provide students with a basic through to more advanced level of understanding of the techniques of modern econometric practices as applied in finance and economic research. It further aims to equip students with the tools required to model, analyse and predict financial market and corporate behaviour and to test theories and hypothesis in this field and provides training in the technical skills necessary to undertake empirical research.

Learning outcomes
Upon completion of this module students will be able to:
- Demonstrate advanced knowledge of econometric tools employed in finance and deploy these appropriately
- Appreciate how financial econometrics is used in current applied literature on financial modelling and forecasting
- Apply econometric tools (software packages) to conduct their own empirical investigations
- Construct and estimate more complex econometric models and be able to apply these techniques to economic data including modelling time-series data and panel data estimation with both cross-section and time-series properties
- Evaluate and critique published empirical papers in the finance journals

Skills outcomes
Upon completion of this module students will be able to:
Transferable
- Communicate effectively in both written and oral environment (including exercising interpersonal communication skills)
- Apply planning, organisation, and time management skills and initiative (including computer literacy and information retrieval) to problem solving, analysis and research
- Utilise core mathematical and statistical skills that underpin econometric analysis in a wider context

Subject Specific
- Apply econometric skills in the field of finance

Syllabus

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.

Modelling Using Multiple Regression. Autocorrelation and Detection.
Dynamic Models and Lagged Variables. Endogeneity and Solutions.
Applications and Examples.

Analysis of Limited Dependent Variables. Probability. Linear probability
Logistic Regression. Models, Tests and Interpretation. Applications in Finance: Credit Risk and Probability of Default. Other Limited Dependent Variables.

Analysing Financial Time- Series. Univariate Models. Autocorrelation and ARIMA processes.

Multivariate Regression with Time-Series Variables. Spurious Regression and Cointegration

Financial Volatility. Random Walks. ARCH and GARCH Models.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	11	2.00	22.00
Seminar	9	1.00	9.00
Private study hours			119.00
Total Contact hours			31.00
Total hours (100hr per 10 credits)			150.00

Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

Opportunities for Formative Feedback

Your teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.

Methods of assessment

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr	100.00
Total percentage (Assessment Exams)		100.00

The resit for this module will be 100% by 2 hours examination

Reading list

The reading list is available from the Library website

Last updated: 03/10/2022

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