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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
Author Profile Icon Dr. Param Jeet
Dr. Param Jeet
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R 2. Statistical Modeling FREE CHAPTER 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

Credit spread


Credit risk is one of the major problems for financial institutions. The major cause for this is credit quality, and credit spread values help to understand credit risk depending upon the credit quality. Credit spread is an important concept in institutional trading as credit spread depends upon the quality or rating of bonds. It is the difference in bond yield of two bonds with similar maturity but with different bond ratings. We are going to use the CreditMetrics package for this, which can be installed and imported to the R workspace using the following two commands:

> install.packages('CreditMetrics') 
> library('CreditMetrics') 

Credit spread is calculated using cm.cs(), which has just two parameters. The first parameter is the one-year migration matrix for some institution or government which issues credit and the second parameter is loss given default (LGD), which means maximum loss if the obligor of credit defaults. Normally, credit with rating AAA is on the top...

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