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Module Title LM Financial Modelling and Forecasting Techniques
SchoolBirmingham Business School
Department Birmingham Business School
Module Code 07 15975
Module Lead Theo Panagiotidis
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites
Co-requisites
Restrictions Students on other Postgraduate Programmes in the Business School may be allowed to take the module if they have GCE A Level Grade C in Mathematics
Contact Hours Lecture-0 hours
Seminar-0 hours
Tutorial-0 hours
Project supervision-0 hours
Demonstration-0 hours
Practical Classes and workshops-0 hours
Supervised time in studio/workshop-0 hours
Fieldwork-0 hours
External Visits-0 hours
Work based learning-0 hours
Guided independent study-0 hours
Placement-0 hours
Year Abroad-0 hours
Exclusions
Description

The module will consider a range of techniques that can be used to forecast operating income, earnings, share prices, interest rate, foreign exchange rate, etc. Some of the techniques that will be considered are linear and no linear regressions with lagged variables, including ARIMA, ARMAX, ARCH (q) and GARCH (pq). Techniques of determining the existence of unit roots, random walks, trends and cointegration will be considered together with those that can be used to correct errors, forecast seasonal and cyclical series, measure accuracy of a forecast, test the stability and non-linearity of market returns on shares, bonds and foreign exchange rates, as well as stimulation. Market microstructure in developed and emerging markets as well as recent studies where these techniques were used, will also be considered.

Learning Outcomes

By the end of the module the student should be able to:

  • apply procedures for model-building in finance;
  • test financial theories and forecast financial variables using real-world data;
  • apply specifically the classical linear regression, limited dependent variable, time series analysis and simultaneous equations models to financial data;
  • identify when the model’s assumptions are satisfied in practice and how to deal with violations;
  • demonstrate comprehensive knowledge and understanding of the theory and application of univariate time series modelling and forecasting using ARMA and ARCH-GARCH techniques.
Assessment 15975-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (75%)
15975-02 : Class Test : Class Test (25%)
Assessment Methods & Exceptions 1500 words (25%) and a 3 hr exam (75%) Reassessment: 3 hour exam (100%)
Other
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