Module: Financial Econometrics

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Financial Econometrics
Module Code	FBA1052
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	5

Description

The course module will help the students understand the basics of Financial Econometrics and its application to Financial Data. It will further include conceptual underpinnings of Regression Models, Financial Time Series Analysis, and their application in the Financial World. The course uses data analysis tools to further strengthen the student's capability to enter the workforce and will thus enhance their employability.

Learning Outcomes

1. 1E6C9D65-02ED-0001-5A73-1B20DC2C6000
2. Understand the concepts of linear model and apply on a real data using data analysis tools
3.
4. 15,6,9
5. 1
6. 1E6C9D65-0BFC-0001-394F-4BEF1AB0B8D0
7. Explore the Financial Time Series, understand the trend, Linear association amongst the time Series
8.
9. 17,15,6,8,11,9
10. 2
11. 1E6C9D65-1C3B-0001-36F6-D040F3302B60
12. Learn to Forecast Univariate and Multivariate Financial Time Series, Understand the concept of Volatility Models
13.
14. 15,6,8,9
15. 3
16. 1E6C9D65-4A4B-0001-DD92-61301CC2DFE0
17. Exposure to Generalized Pareto Distribution for modeling extremities, and concepts such as Monte Carlo Simulation and Bootstrapping
18.
19. 17,12,15,6,8,11,9
20. 4
21. 1E6C9D65-7701-0001-41AA-1B90BC80197A
22. Exposure to High Frequency Data and market Microstructure with utility of Machine Learning and Deep Learning Models
23.
24. 17,12,15,6,8,11,9
25. 5

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	No Description
Tutorial	10	No Description
Directed learning	54	No Description
Independent Study	37.5	No Description
Independent Study	50	Preparation for test and assignment
Total Workload: 187.5

Section Breakdown
CRN	21473	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Pawan Kumar	Module Teacher

Section Breakdown
CRN	12238	Part of Term	Semester 1
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator		Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	n/a	30%	n/a
Assignment	n/a	40%	n/a
Group presentation	n/a	30%	n/a
Written Exam	n/a	0%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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, time series modelling and forecasting
Autocorrelation, skew, kurtosis, time aggregation, volatility clustering, long memory, leverage, trading volume.: Moving average processes, Autoregressive processes, ARMA processes Financial Time Series Forecasting, Vector Autoregression for Multivariate Forecasting

Volatility
Nonparametric measurement, Volatility Persistence, ARCH effect in Volatility series, GARCH-type models, forecasting, news impact curve, stochastic volatility, option implied volatility

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

Simulation methods
Monte Carlo simulations, Variance reduction techniques, Bootstrapping, Random number generation

Ultra high frequency data
Market Microstructure, stylized facts, bid-ask bounce, irregularly spaced data, realized variance, jumps, Machine LEARNING

Indicative Reading List

Books:

Rue Tsay: 2010, Analysis of Financial Time Series, 3rd, Wiley, 978-0-470-414
Xin Guo,Tze Leung Lai,Howard Shek,Samuel Po-Shing Wong: 2017, Quantitative Trading, CRC Press, Taylor & Francis Group, CRC Press is, 0, 9781498706483
Matthew F. Dixon,Igor Halperin,Paul Bilokon: 2020, Machine Learning in Finance, Springer, 548, 3030410676

Articles:

Robert F. Engle: 1982, Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50, 21, https://econpapers.repec.org/article/ecmemetrp/v_3a50_3ay_3a1982_3ai_3a4_3ap_3a987-1007.htm, 524172
2002: Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, https://www.sciencedirect.com/science/article/abs/pii/0304407686900631, 524173, 1
Estimating continuous-time stochastic volatility models of the short-term interest rate: Journal of Econometrics, 77, 343, https://www.sciencedirect.com/science/article/abs/pii/S0304407696018192,

Other Resources

1: Website, European Central Bank, 2018, Bank to sovereign risk spillovers across borders: evidence from the ECB’s Comprehensive Assessment,
420403: 1, Website, Bank of International Settlements, 2019, How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm,
https://www.bis.org/publ/work834.pdf: