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Python for Finance

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

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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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

Using the KMV model to estimate the market value of total assets and its volatility

KMV stands for Kealhofer, McQuown and Vasicek who founded a company focusing on measuring default risk. KMV methodology is one of the most important methods to estimate the probability of default for a given company by using its balance sheet information and the equity market information. The objective of this section is to show how to estimate the market value of total assets (A) and its corresponding volatility (σA). The result will be used later in the chapter. The basic idea is to treat the equity of a firm as a call option and the book value of its debt as its strike price. Let's look at the simplest example. For a firm, if its debt is $70 and equity is $30, then the total assets will be $100, see the following table:

100

70

30

Assume that the total asset jumps to $110 and the debt remains the same. Now, the value of the equity increases to $40. On the other hand, if the assets drop to...

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