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â—„Faculty of Economics and Administrationâ–ş



                                    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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