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Python for Finance

You're reading from Python for Finance Apply powerful finance models and quantitative analysis with Python

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787125698

Length 586 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Table of Contents (23) Chapters

Python for Finance Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Python Basics FREE CHAPTER

2. Introduction to Python Modules

3. Time Value of Money

4. Sources of Data

Diving into deeper concepts

5. Bond and Stock Valuation

6. Capital Asset Pricing Model

7. Multifactor Models and Performance Measures

8. Time-Series Analysis

9. Portfolio Theory

10. Options and Futures

11. Value at Risk

12. Monte Carlo Simulation

13. Credit Risk Analysis

14. Exotic Options

15. Volatility, Implied Volatility, ARCH, and GARCH

Index

Exercises

What is the definition of volatility?
How can you measure risk (volatility)?
What are the issues related to the widely used definition of risk (standard deviation)?

How can you test whether stock returns follow a normal distribution? For the following given set of stocks, test whether they follow a normal distribution:

Company name	Ticker	Dell company	DELL
International Business Machine	IBM	General Electric	GE
Microsoft	MSFT	Google	GOOG
Family Dollar Stores	FDO	Apple	AAPL
Wal-Mart Stores	WMT	eBay	EBAY
McDonald's	MCD

What is the lower partial standard deviation? What are its applications?
Choose five stocks, such as DELL, IBM, Microsoft, Citi Group, and Walmart, and compare their standard deviation with LPSD based on the last three-years' daily data.
Is a stock's volatility constant over the years? You could choose International Business Machine (IBM) and Walmart (WMT) to test your hypothesis.
What is an ARCH (1) process?
What is a GARCH (1,1) process?
Apply the...

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