Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Exercises


  1. What is the definition of volatility?

  2. How can you measure risk (volatility)?

  3. What are the issues related to the widely used definition of risk (standard deviation)?

  4. 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

      
  5. What is the lower partial standard deviation? What are its applications?

  6. 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.

  7. Is a stock's volatility constant over the years? You could choose International Business Machine (IBM) and Walmart (WMT) to test your hypothesis.

  8. What is an ARCH (1) process?

  9. What is a GARCH (1,1) process?

  10. Apply the...