Python Guide for Introductory Econometrics for Finance

by Chris Brooks

Overview

This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings.

Product Details

ISBN-13:	9781108860130
Publisher:	Cambridge University Press
Publication date:	03/28/2019
Sold by:	Barnes & Noble
Format:	eBook
Sales rank:	349,228
File size:	22 MB
Note:	This product may take a few minutes to download.

About the Author

Chris Brooks is Professor of Finance and Director of Research at the ICMA Centre, Henley Business School, University of Reading, where he also obtained his Ph.D. He has diverse research interests and has published over a hundred articles in leading academic and practitioner journals, and six books. He is Associate Editor of several journals, including the Journal of Business Finance and Accounting, the International Journal of Forecasting and the British Accounting Review. He acts as consultant and advisor for various banks, corporations and professional bodies in the fields of finance, real estate, and econometrics.

Table of Contents

1. Getting started; 2. Data management in Python; 3. Simple linear regression – estimation of an optimal hedge ratio; 4. Hypothesis testing – example 1: hedging revisited; 5. Estimation and hypothesis testing – example 2: the CAPM; 6. Sample output for multiple hypothesis tests; 7. Multiple regression using an APT-style model; 8. Quantile regression; 9. Calculating principal components; 10. Diagnostic testing; 11. Constructing ARMA models; 12. Forecasting using ARMA models; 13. Estimating exponential smoothing models; 14. Simultaneous equations modelling; 15. The Generalised method of moments for instrumental variables; 16. VAR estimation; 17. Testing for unit roots; 18. Cointegration tests and modelling cointegrated systems; 19. Volatility modelling; 20. Modelling seasonality in financial data; 21. Panel data models; 22. Limited dependent variable models; 23. Simulation methods; 24. The Fama-MacBeth procedure; 25. Using extreme value theory for VaR calculation.