Module: Financial Econometrics

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Financial Econometrics
Module Code	EF5162 (ITS) / FBA1024 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	-
Module Teachers	-
NFQ level	9	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

None
Resubmit take home assignments

Description

Introduction to the theory and practice of financial time series analysis. Students will learn statistical methods to characterize empirical features of financial time series data, apply relevant time series methods to analyze data using appropriate statistical software, critically evaluate empirical results reported in academic journal articles.

Learning Outcomes

1. analyse financial time series data using appropriate statistical methods and software
2. explain theoretical properties of financial time series models
3. validate past empirical analyses published in academic journals
4. apply financial econometric techniques to quantitatively address practical issues in finance

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	lectures and in-class programming
Independent Study	72	readings and review of lectures
Assignment Completion	79.5	review for in-class test and submission of assignment reports
Total Workload: 187.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Empirical characteristics of asset returns
autocorrelation, skew, kurtosis, time aggregation, volatility clustering, long memory, leverage, trading volume.

Volatility
nonparametric measurement, GARCH-type models, forecasting, news impact curve, stochastic volatility, option implied volatility.

Ultra high frequency data
market microstructure, stylized facts, bid-ask bounce, irregularly spaced data, realized variance, jumps.

Statistics of extremes
extreme value theory, generalized extreme value distribution, threshold exceedance, generalized Pareto distribution.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Take home assignments	60%	As required
In Class Test	In-class test	40%	As required

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Ruey Tsay: 0, Analysis of Financial Time Series, 3rd,
Stephen Taylor: 0, Asset Price Dynamics, Volatility, and Prediction,
Alexander McNeil and Rudiger Frey and Paul Embrechts: 0, Quantitative Risk Management, revised,

Other Resources

None

Module for new MSc in Finance as approved by Programme Board 26th March 2014. Submitting final document for T&L to Jonathan 28th March 2014.