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

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...