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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

Simulating a GARCH (p,q) process using modified garchSim()

The following code is based on the R function called garchSim(), which is included in the R package called fGarch. The authors for fGarch are Diethelm Wuertz and Yohan Chalabi. To find the related manual, we perform the following steps:

  1. Go to http://www.r-project.org.
  2. Click on CRAN under Download, Packages.
  3. Choose a close-by server.
  4. Click on Packages on the left-hand side of the screen.
  5. Choose a list and search for fGarch.
  6. Click on the link and download the PDF file related to fGarch.

The Python program based on the R program is given as follows:

import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
#
sp.random.seed(12345) 
m=2
n=100              # n is the number of observations
nDrop=100          # we need to drop the first several observations 
delta=2
omega=1e-6 
alpha=(0.05,0.05)
#
beta=0.8 
mu,ma,ar=0.0,0.0,0.0
gamma=(0.0,0.0) 
order_ar=sp.size(ar) 
order_ma=sp.size(ma) 
order_beta=sp.size(beta)
#
order_alpha =sp.size...
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