Topic outline

  • General

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    Welcome to ECOM073

    The module will reaffirm the student's understanding of the classical techniques of regression analysis, which will be extended to encompass financial data modelling. The module will also cover the techniques of time series modelling. It will begin by analysing classical linear stochastic models that are formulated in discrete time. It will proceed to analyse models in continuous time that are a feature of modern financial analysis.

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  • Gradebook

    View all my marks and feedback in Gradebook

  • Final Exam

    Weight in final mark: 80%.

    Further details will be provided closer to the time of the final exam.


  • Week 1 - Introductory Lecture Wednesday Jan 24

    Welcome to Week 1!

    This week we would like you to complete:

    • Attend the live lecture   (overview  of  the course)                                               
    • 9am--    Wednesday, Jan 24,     Place: Bancroft: 3.26
    • Read  attached  Introductory Lecture material on this   module 
    • Content topics for this week include:    general introduction to  the course , weekly work, books, teaching material, examples and  why  Financial Time  Series are so   interesting and  useful for  your  further  work, e.g. writing your MSc  dissertations.  This  will  help  you  selecting your optional Semester B modules 

  • Week 2 (Lecture 1, Wednesday Jan 31)

    This week we would like you to complete:

    • Attend the live Lecture 1, January 31,  9am--11 am    Wednesday,  Place: Bancroft: 3.26
    • Study the  Lecture  1  material and handout 
    • Attempt the mini problems of the Quiz 1
    • Content topics for this week Lecture 1 include: Graphical analysis, plots, impulse response  function


  • Week 3 (Lecture 2, February 7, Wednesday)

    This week we would like you to complete:

    • Attend the live Lecture 3,  9am--    Wednesday, 7 Febr, Place: Bancroft: 3.26
    • Attend Class/Tutorial       11:00-12:00 Thursday, 8 Febr, Place: QB 212 PC lab
    • Study the  Lecture  2  material and handout 
    • Attempt the mini problems of the Quiz 2
    • Attempt the  problem set 1 of Tutorial 1 (results released next week)

    • Content topics for this week Lecture 2 include:  Introductory concepts. Stylized facts of financial data.  Summary statistics. Testing for symmetry and heavy tails. Stationarity, autocorrelation function, white noise sequence 


  • Week 4 (Lecture 3, Feb 14, Wednesday)

    This week we would like you to complete:

    • Attend the live Lecture 3
    • Study the  Lecture  3  material and handout 
    • Attempt the mini problems of the Quiz 3
    • Attempt the  Problem Set 2 of Tutorial 2 (results released next week)

    • Content topics for this week Lecture 3:  Testing for  correlation. AR, MA and ARMA models.


  • Week 5 (Lecture 4, Feb 21, Wednesday)

    Change of   Lecture  room:

    Lecture will take place in  PL-301 (Peter Landin teaching rooms) 

    It’s number 10 on the map link here:

    Qmul Ac

    Wednesday 9:00-11:00am

    This week we would like you to complete:

    • Attend the live Lecture 4 
    • Study the  Lecture  4  material and handout 
    • Attempt the mini problems of the Quiz 4
    • Attempt the  Problem Set 3 of Tutorial 3 (results released next week)

    • Content topics for this week include:  order  selection for AR(p), MA(q), ARMA(p,q) model. Finding  the  best  fitting model. Model selection criteria. Properties of  AR(1) model


  • Week 6 (Lecture 5, Feb 28, Wednesday)

    Lecture will take place in  PL-301 (Peter Landin teaching rooms)

    This week we would like you to complete:

    • Attend the live Lecture 5
    • Study the  Lecture  5  material and handout 
    • Attempt the mini problems of the Quiz 5
    • Attempt the  Problem Set 4 of Tutorial 4 (results released next week)

    • Content topics for this week include: Model selection and forecasting


  • Week 7: Reading Week. Preparation for Midterm test

    ECOM073 Midterm test

    Wednesday, 27 March, 9:00am-10:00am (60 minutes)

    Place:  PL-301 (Peter Landin teaching rooms)


    Preparation: read the Info  on Midterm, see attached solutions of  quizzes 2-4


  • Week 8 (Lecture 6, March 13)

    This week we would like you to complete:

    • Attend the live Lecture 6
    • Study the  Lecture  6  material and handout 
    • Attempt the mini problems of the Quiz 6
    • Attempt the  Problem Set 5 of Tutorial 5 (results released next week)

    • Content topics for this week include:  Non-stationary processes, unit root models, testing for unit root.


  • Week 9

    This week we would like you to complete:

    • Attend the live Lecture 7
    • We  will   cover  Unit root model (see Lecture  6) 
    • Prepare  for  the  midterm  test on March  27, see  information Week 7 section
    • Attempt the  Problem Set 6 of Tutorial 6 (results released next week)

    • Content topics for this week include: Unit root model.


  • Week 10 Wednesday 27 March. Midterm test

    Midterm test:  Wednesday, 9:00-10:00,  March  27, 2023 (in person)

    Test will take place in  PL-301 (Peter Landin teaching rooms)

    Preparation for  the  Midterm test:  see material  in  Week 7 on QMPLUS


  • Week 11 Wednesday, April 3

    This week we would like you to complete:

    • Attend the  Lecture 9
    • Study the  Lecture  9  material and handout 

    • Content topics for this week include: Introduction to conditional heteroscedasticity: ARCH, GARCH models.  


  • Week 12 (Wednesday 10 April)

    This week we would like you to complete:

    • Attend the Lecture. 
    • Study the  Lecture  9, 8  material and handout 
    • Attempt the   Tutorial 8 
    • Discuss preparation for the  final exam
    • Content topics for this week include:   GARCH, Seasonal models, VAR models


  • Preparation for the final Exam ECOM073

    Preparation   for  the final Exam  ECOM073

  • List of Topics (module weekly syllabus)

    SCHEDULE - 

    Lecture:  Wednesday 9:00-11:00 am    Bancroft: 3.26                 (Liudas Giraitis)
    Class-Tutorial:  Thursday 11:00-12:00pm  QB 212 PC lab (Claudio Vallar)

    Week 1,   24 January,   Introduction to the course
    Week 2,   31 January, (lecture 1, no tutorial)                      )
    Week 3,   7 February, (lecture 2,  Tutorial 1)
    Week 4,  14 February (lecture 3,  Tutorial 2)
    Week 5,   21 February, (lecture 4,  Tutorial 3)
    Week 6,   28 February, (lecture 5,  Tutorial 4)

    Week 7, 6 March, Reading week [no teaching]

    Week 8,   13 March (lecture 6,  Tutorial 5)
    Week 9,    20 March (lecture 7, Tutorial 6)
    Week 10, 27 March Midterm test [9:00-10:00]  (+ Lecture 8, 45min]
    Week 11,  3 April    (lecture 9,  Tutorial  7)
    Week 12,  10 April   (lecture 10,  Tutorial general overview)

    The outline of the course 

    It is based on the following syllabus. Some of the topics will be carried over consecutive weeks: 

    1.  Graphical analysis, plots, impulse response  function

    2.  Introductory concepts. Stylized facts of financial data.  Summary statistics. Testing for symmetry and heavy tails. Stationarity, autocorrelation function, white noise sequence 

    3.  Testing for  correlation. AR, MA and ARMA models.

    4.  Linear time series: a) Autoregressive process. AR(1) and AR(2) models: properties and prediction, business cycle. Order determination. Estimation of an AR(1) model. Forecasting. 

    5.  Model selection and forecasting

    6.  Non-stationary processes, unit root models, testing for unit root.

    7. Seasonal time series models 

    8 . Introduction to conditional heteroscedasticity: ARCH, GARCH models.  

    9  Regression models with times series errors. Co-integration. Multivariate time series models

  • Reading Materials

    Basic readings: 

    Lecture Notes 

    R. S. Tsay. Analysis of Financial Time Series. 2nd edition, Wiley, 2005. 

    S. Bisgaard and M Kulahci. Time Series Analysis and forecasting by example. Wiley, 2011

    Further readings: 

    Brockwell P. J. and R. A. Davis, Introduction to Time Series and Forecasting, Springer Texts in Statistics, 2002.

  • Where to get help

    Here is a list of common queries and who to contact:

    Contact the PG administration team at Email "pgsefsupport@qmul.ac.uk" or in person at Graduate Centre 3.06 about:

    • Class swap forms
    • Issues with class allocations
    • Timetable clashes
    • Module registration
    • Examination/assessment queries
    • Attendance concerns
    • Technical issues regarding submitting assignments
    • Extenuating Circumstances
    • Student support
    • Social events and administrative support for student led activities


    Contact the Module Organiser and Teaching Assistants (TAs) about:

    • Academic content
    • General assessment questions
    • Office hours
    • Feedback on work or assessments

    • QMplus support for students URL

      This link has information to help students to get the best out of QMPlus - with helpful guides and videos.

  • Hints and Tips

    Below are some guides you may find useful for assignments, they all open in a new window:


    If you need to submit an Extenuating Circumstances form, please visit the QMUL Guidelines or contact the Student Support mailbox: pgsefsupport@qmul.ac.uk

    • QMplus Support for Students URL

      This link has information to help students to get the best out of QMPlus - with helpful guides and videos.