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Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Financial Econometrics
Module Code	EF586
School	DCUBS
Online Module Resources
Module Teacher	William Kelly
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This module is to introduce students to the theory and practice of financial time series anaylsis. The emphasis is on the application of statistical methods to analyze financical time series data. Students will develop knowledge and skills in empirical features of financial time series data, applying relevant time series methods to analyze data, using appropriate statistical software to analyze data, critically evaluating empirical results reported in journal articles. Students will attend and participate in lectures, familiarize themselves with the use of statistical software, work on take home assignments that replicate some previous empirical research, critically evaluate some of the literature in financial econometrics.

Learning Outcomes

1. Analyse financial time series data using appropriate statistical methods
2. Explain theoretical properties of financial time series models
3. Validate past empirical analyses published in academic journals
4. Present results of statistical analyses using appropriate statistical displays (e.g. tables and graphs)

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	lecture and discussion
Lab	10	computer labs
Independent learning	31	readings and review of lectures
Assignment	30	take home assignments
Assignment	30	class test preparation
Total Workload: 125

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/GARCH-M models, forecasting, news impact curve, stochastic volatility, option implied volatility..

Ultra high frequency data.
market microstructure, stylized facts, bid-ask bounce model (Roll 1984), irregularly spaced data, ACD models..

Realized variance.
microstructure noise, sampling methods, jumps..

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

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Assignment	take home assignments	50%	As required
In Class Test	in-class test	50%	As required

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Ruey Tsay: 0, Analysis of Financial Time Series, 2nd,
Stephen Taylor: 0, Asset Price Dynamics, Volatility, and Prediction,
Alexander McNeil and Rudiger Frey and Paul Embrechts: 0, Quantitative Risk Management,

Other Resources

None

Array

Programme or List of Programmes

MFCM

MSc in Finance

PBSSA

PG Exchange(Business School)

PBSSAO

PG Study Abroad(Business School)

Timetable this semester: Timetable for EF586

Date of Last Revision