Introductory Econometrics for Finance - Brooks, Chris

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Résumé :
A complete econometrics resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner. It shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of the previous editions, the fourth edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods for those looking to extend their understanding. New material on state space models and the Kalman filter, extreme value theory for value at risk modelling, and the generalised method of moments, ensures that students hove all the tools they need to conduct sophisticated empirical work in finance. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support material with software guides for EViews, Stata, R and Python. Online Resources (cambridge.org/brooks4e) : answers to end-of-chapter questions . instructor-only multiple-choice test question banks . data used in examples . lecture slides in both PowerPoint and LaTeX formats . additional chapter review questions, in multiple-choice format, with answers and feedback provided . video lectures covering some of the core material . detailed accompanying software manuals for the major econometrics packages (EViews, Stata, R, Python) . an extensive glossary . links to useful websites.

Sommaire:
Preface to the fourth edition; 1. Introduction and mathematical foundations; 2. Statistical foundations and dealing with data; 3. A brief overview of the classical linear regression; 4. Further development of classical linear regression; 5. Classical linear regression model assumptions; 6. Univariate time-series modelling and forecasting; 7. Multivariate models; 8. Modelling volatility and correlation; 10. Switching and state space models; 11. Panel data; 12. Limited dependent variable models; 13. Simulation methods; 14. Additional econometric techniques for financial research; 15. Conducting empirical research; Appendix 1. Sources of data used in this book and the accompanying software manuals; Appendix 2. Tables of statistical distributions; Glossary; References; Index.