Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python for Finance

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

Arrow left icon
Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 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

  1. What is the definition of exotic options?
  2. Why is it claimed that a callable bond is equivalent to a normal bond plus a Bermudan option (the issuing company is the buyer of this Bermudan option while the bond buyer is the seller)?
  3. Write a Python program to price an Asian average price put based on the arithmetic mean.
  4. Write a Python program to price an Asian average price put based on the geometric mean.
  5. Write a Python program to price an up-and-in call (barrier option).
  6. Write a Python program to price a down-and-out put (barrier option).
  7. Write a Python program to show the down-and-out and down-and-in parity.
  8. Write a Python program to use permutation() from SciPy to select 12 monthly returns randomly from the past five-year data without placement. To test your program, you can use Citigroup and the time period January 1, 2009 to December 31, 2014 from Yahoo Finance.
  9. Write a Python program to run bootstrapping with n given returns. For each time, we select m returns where m>n.
  10. In this...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime