| Module Title |
Financial Econometrics |
| Module Code |
FBA1052 |
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Faculty |
DCU Business School |
School |
DCU Business School |
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NFQ level |
9 |
Credit Rating |
5 |
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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.
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Learning Outcomes
1. Understand the concepts of linear model and apply on a real data using data analysis tools
2. Explore the Financial Time Series, understand the trend, Linear association amongst the time Series
3. Learn to Forecast Univariate and Multivariate Financial Time Series, Understand the concept of Volatility Models
4. Exposure to Generalized Pareto Distribution for modeling extremities, and concepts such as Monte Carlo Simulation and Bootstrapping
5. Exposure to High Frequency Data and market Microstructure with utility of Machine Learning and Deep Learning Models
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| 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 |
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| 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 | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| 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
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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
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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
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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,
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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:
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