澳门六合彩开奖记录
ICM204-Financial Econometrics
Module Provider: ICMA Centre
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: ICM337 Econometric Analysis for Finance or REMF37 Quantitative Techniques
Modules excluded:
Current from: 2023/4
Module Convenor: Prof Mike Clements
Email: m.p.clements@icmacentre.ac.uk
Type of module:
Summary module description:
Building on the material introduced in Econometric Analysis for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood estimation and two-stage least squares, models of volatility, simulation techniques, and multivariate models. Case studies from the academic finance literature are employed to demonstrate potential uses of each approach. Extensive use is also made of financial econometrics software to demonstrate how the techniques are applied in practice.
Aims:
To provide students with a critical understanding of modern econometrics, with an emphasis on financial applications. To enable students to analyse data, estimate systems of equations for data which might be stochastically non-stationary, or simultaneously determined, and to model conditional variances. The aim is to appreciate the challenges (and opportunities) of time-series听 and panels of data for discovering empirical 鈥渇acts鈥 about the economic and financial system.听
Assessable learning outcomes:
By the end of the module, it is expected that the student will be able to
- Apply听 a number of different approaches to modelling and forecasting to financial data
- Critically evaluate alternative models and methods for addressing听 particular problems in empirical finance (e.g., forecasting), and to analyse any limitations of the approach
- To be able to critically evaluate the use of econometrics in the published academic finance literature
Additional outcomes:
The module also aims to encourage the development of IT skills and in particular the manipulation of data using statistical software packages. Students will also improve their ability to translate abstract theoretical concepts into practical solutions to financial problems.
Outline content:
Topic 1 Univariate time-series modelling and forecasting
Topic 2 Simultaneous equations models
- Simultaneous equations bias
- Identification
- Estimation, triangular systems
Topic 3 Vector autoregressive models听
- Motivation, formulation, estimation
- Comparison with structural models
- Causality, impulse response functions, variance decompositions
Topic 4 Multivariate cointegration
- Unit roots, cointegration and spurious regression听听
- Multivariate cointegration: estimation and hypothesis testing
Topic 5 Volatility modelling and forecasting
- Maximum likelihood estimation听
- Volatility modelling using autoregressive conditionally heteroscedastic (ARCH) models听
- variants and extensions of the ARCH model
Topic 6 Panel data analysis
- fixed effects
- random effects
- Dynamic models听听
Topic 7 Simulations methods in econometrics and finance
- motivation
- pure simulation versus bootstrap
- variance reduction techniques
Brief description of teaching and learning methods:
Core lectures supported by lab based computer seminars and classroom based tutor led discussion
听 | Autumn | Spring | Summer |
Lectures | 18 | ||
Seminars | 8 | ||
Practicals classes and workshops | 0 | ||
Guided independent study: | 听 | 听 | 听 |
听 听 Wider reading (independent) | 30 | ||
听 听 Exam revision/preparation | 30 | ||
听 听 Advance preparation for classes | 20 | ||
听 听 Revision and preparation | 22 | ||
听 听 Carry-out research project | 32 | ||
听 听 Reflection | 40 | ||
听 | 听 | 听 | 听 |
Total hours by term | 0 | 200 | 0 |
听 | 听 | 听 | 听 |
Total hours for module | 200 |
Method | Percentage |
Written exam | 60 |
Project output other than dissertation | 40 |
Summative assessment- Examinations:
One 3 hour written exam
The examination for this module will require a narrowly defined time window and is likely to be held in a dedicated exam venue.
Summative assessment- Coursework and in-class tests:
One Group Project (4-5 students), to be submitted in the听 last week of the Spring term or the first week of the Easter vacation. The maximum word count is 2,500.
Formative assessment methods:
Practice MCQ tests via Blackboard.
Penalties for late submission:
In accordance with the University policy.听
Assessment requirements for a pass:
50% weighted average mark
Reassessment arrangements:
By written examination only, as part of the overall examination arrangements for the MSc programme.
Additional Costs (specified where applicable):
Required textbooks:
Brooks, C. (2019). Introductory Econometrics for Finance, 4th Edition, Cambridge University Press, Cambridge (UK)
拢45 (but available as e-book via UoR Library)
听
Specialist equipment or materials: Calculator
Recommended Models:
1. Casio FX-83GTx or Casio FX-83GTPLUS听
2. Casio FX-85GTx or Casio FX-85GTPLUS
3. Casio FX-85MS
4. Texas Instruments BA II Plus
拢15
Last updated: 30 March 2023
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.