Studying at York
Financial Econometrics - ECO00084M
- Department: Economics and Related Studies
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2023-24
- See module specification for other years: 2024-25
Module summary
To introduce the main techniques involved in the modelling of financial data with sufficient coverage of the principles of estimation and inference to prepare for more advanced techniques that are becoming popular in modern empirical research
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2023-24 |
Module aims
To introduce the main techniques involved in the modelling of financial data with sufficient coverage of the principles of estimation and inference to prepare for more advanced techniques that are becoming popular in modern empirical research, such that examples of such research can be followed, understood and appraised;
To demonstrate, and to deliver the experience of, application to real world financial data using dedicated software.
Module learning outcomes
After successful completion of the module students will be able to:
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(If to be covered in this module) Collect a real data set for analysis;
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Demonstrate some of the key results that have guided the development of financial econometrics;
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Solve a range of technical and data problems within financial econometrics;
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Carry out estimation and inference using a range of models for cross-section and time series data in finance, using appropriate software (Eviews), taking account of common features of such data, including non-stationarity, serial correlation and changing volatility;
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Appreciate the assumptions and underlying least squares, maximum likelihood and generalised method of moments estimation and their relation to common theories in finance;
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Carry out formal tests of those assumptions; and hence,
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Evaluate the appropriateness of a given technique for modelling financial data;
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Follow and critically appraise empirical methods discussed in the Finance literature.
Module content
A rough outline
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Statistics refresher and Matrix Algebra.
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Simple regression: assumptions; properties; inference (t-test) and prediction.
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Multiple regression: assumptions; properties; inference (t & F-test) and prediction.
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Further multiple regression, with matrix algebra.
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Serial Correlation: effects; testing; Heteroskedasticity: effects; testing;
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Endogeneity. Estimation frameworks: Maximum likelihood and GMM.
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Univariate time series: Stationarity, ARMA models, basic forecasting.
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Unit root testing, spurious regression.
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Multivariate Time Series: VAR; Granger Causality; Cointegration.
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Modelling volatility: GARCH.
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Further topics such as: Factor models; Quantile models and Value at Risk; Switching; Limited Dependent Variables.
Indicative assessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 70 |
| Essay/coursework | 30 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 70 |
| Essay/coursework | 30 |
Module feedback
Students have access to feedback on individual assessments. General cohort assessment feedback is posted on the VLE after the marking is complete.
Indicative reading
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Heij, DeBoer, Franses, Kloek and van Dijk Econometric Methods with Applications in Business and Economics.
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Brooks - Introductory Econometrics for Finance (as a bit of a back-up and good on applications).
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Campbell, Lo and MacKinlay - The Econometrics of Financial Markets (to be used very selectively, good link to finance theory).
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Enders - Applied Econometric Time Series.
1, 2 and 4 are based around Eviews.