Page 60 - PROSPECTUS FEA IT 17-18

◄Faculty of Economics and Administration►

EQC7013 OPERATIONS RESEARCH METHODS

Learning Outcomes At the end of this course, the students are able to:

(1) Explain various modeling techniques and problem
structuring methods in operations research;
(2) Utilize quantitative models in decision making and
problem solving; and
(3) Solve the quantitative models using computer software.

Synopsis of Course Operations Research, also referred to as Management
Contents Science, is a practical and scientific approach to problem
solving utilizing quantitative techniques. This course covers
several analytical methods including linear programming,
network analysis, project scheduling, decision analysis and
waiting line analysis. These methods can be used to
analyse complex problems and improve decision making
processes in industry, business and the public sector.

Assessment Continuous Assessment : 50%
Final Examination : 50%
Main Reference (1) Andersen, D.R., Sweeney, D.J., Williams, T.A. and
Martin, K. (2011). An Introduction to Management
Science: Quantitative Approaches to Decision Making.
th
13 ed., South-Western.
(2) Hillier, F. S., and Hillier, M.S. (2010). Introduction to
Management Science: A Modelling and Case Study
nd
Approach with Spreadsheets, 4 . ed., McGraw-Hill.
EQC 7014 APPLIED FINANCIAL ECONOMETRICS

Learning Outcomes At the end of the course, students are able to:
(1) Analyse returns to financial assets and construct
indices as measures of stock market performance;
(2) Design financial models including time-varying volatility
models using appropriate software;
(3) Determine the adequacy of estimated econometric-time
series models in the area of finance; and
(4) Communicate the findings effectively.
Synopsis of Course The course introduces the methods of construction of stock
Contents market indices, computation of returns with adjustment for
capital changes and estimation of betas. Tests of market
efficiency and estimation of selected financial models are
discussed. The capital asset pricing model is applied for
analyzing the ability of market timing and stock selectivity.
Spurious regressions and stochastic processes are
introduced. The importance of data stationarity and order of
integration for financial data is explained. VAR modelling,
impulse response function, variance decomposition,
causality, cointegration and error correction mechanism are
discussed in the context of financial models. Calendar
anomalies and methods for modelling volatility in financial
data, such as ARCH & GARCH, are discussed.
Assessment Continuous Assessment: 50%
Final Examination: 50%

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