BE372-7-AU-CO:
Financial Time Series: Methods and Applications

The details
2023/24
Essex Business School
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Monday 09 October 2023
Friday 15 December 2023
20
14 July 2023

 

Requisites for this module
(none)
(none)
(none)
EC965

 

(none)

Key module for

MSC N34212 Financial Engineering and Risk Management,
MSC N34224 Financial Engineering and Risk Management

Module description

This module aims to provide a masters level module for students in Essex Business School which provides an up-to-date and rigorous course in financial time series econometrics.


This module will introduce students to the core methodology and empirical practice of modern financial time series econometrics.  This will involve developing relevant statistical and mathematical skills, together with the ability to apply these methods to real data using industry-standard software and to interpret their results.

Module aims

The aims of this module are:



  • To provide training in relevant statistical techniques required to perform robust research in financial econometrics, and to illustrate the applicability of these techniques to financial data.

  • To provide students with hands on experience of implementing the methods commonly employed in the financial econometrics literature through practical sessions in which they will be trained in the use of relevant computing software.

  • To develop and transmit knowledge of recent advancements in the financial econometrics literature through examining key papers that develop and/or utilise the econometric techniques covered in the course material.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Have an understanding of the relevant statistic techniques appropriate for performing both theoretical and empirical analysis in financial econometrics.

  2. Have developed detailed knowledge on the use of a relevant statistical software package, being able to use this software to perform advanced statistical techniques on either user imported or pseudo-data.

  3. Be able to critically evaluate contributions made by recent papers in the financial econometrics literature, and to replicate the findings outlined in these papers.

Module information

Syllabus



  • Univariate Stationary Time Series I: Definition of time series processes; Stationarity and invertibility; Autocovariance and autocorrelation functions; Autoregressive Moving Average (ARMA) processes.

  • Univariate Stationary Time Series II: The Wold decomposition; The lag operator; Common factors; Aggregation of time series processes; Estimation of ARMA models.

  • Univariate Stationary Times Series III: ARMA Model selection - Identification, Diagnostic Checking, Information Criteria; Forecasting in ARMA models – Optimal Forecasts, Estimation Effects.

  • Univariate Non-Stationary Time Series: Difference stationary (integrated) processes and trend stationary processes; ARIMA processes; Drifts and trends; Properties of unit root series; Spurious regressions.

  • Unit Root and Stationarity Testing: (Augmented) Dickey Fuller unit root tests; Lag selection and deterministic components; KPSS stationarity tests.

  • Rational bubble detection: Explosive autoregressive processes; Testing for bubbles; Historical detection and real-time detection of bubbles.

  • Time Series Models of Volatility: Martingale difference processes; Conditional variance models: GARCH and ARSV models.

  • Multivariate Time Series Models: VARMA, VMA and VAR models; VARIMA models; Granger causality.

  • Cointegration and error correction models: Definitions; Interpretation; Estimation and Testing.

  • Predictive Regression Modelling: Testing for predictability of financial returns; Dealing with persistence and endogeneity in the data.

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture per week.
  • One 1-hour seminar per week.
  • One 1-hour computer class per week.

Bibliography

This module does not appear to have any essential texts. To see non - essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   2,000 word essay  26/01/2024  100% 
Exam  Main exam: In-Person, Open Book, 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book, 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%

Reassessment

Coursework Exam
30% 70%
Module supervisor and teaching staff
Dr Sam Astill, email: sastill@essex.ac.uk.
Dr Sam Astill & Dr Ilias Chronopoulos

 

Availability
No
No
Yes

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
Of 38 hours, 35 (92.1%) hours available to students:
3 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information
Essex Business School

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