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

VaR for portfolios


In Chapter 9, Portfolio Theory, it was shown that when putting many stocks in our portfolio, we could reduce or eliminate firm-specific risk. The formula to estimate an n-stock portfolio return is given here:

Here Rp,t is the portfolio return at time t, wi is the weight for stock i, and Ri, t is the return at time t for stock i. When talking about the expected return or mean, we have a quite similar formula:

Here, is the mean or expected portfolio return, is the mean or expected return for stock i. The variance of such an n-stock portfolio is given here:

Here, is the portfolio variance, σi,j is covariance between stocks i and j; see the following formula:

The correlation between stocks i and j, ρi,j, is defined here:

When stocks are not positively perfectively correlated, combining stocks would reduce our portfolio risk. The following program shows that the VaR of the portfolio is not simply the summation or weighted VaR of individual stocks within the portfolio:

from matplotlib...