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

Efficiency, Quasi-Monte Carlo, and Sobol sequences


When applying the Monte Carlo simulation to solve various finance-related problems, a set of random numbers is generated. When the accuracy is very high, we have to draw a huge amount of such random numbers. For example, when pricing options, we use very small intervals or a large number of steps to increase the accuracy of our solutions. Thus, the efficiency of our Monte Carlo simulation would be a vital issue in terms of computational time and costs. This is especially true if several thousand options are to be priced. One way to increase the efficiency is to apply a better algorithm, that is, optimize our codes. Another way is to use some special types of random numbers that are more evenly distributed. This is called Quasi-Monte Carlo Simulation. A typical example is a so-called Sobol sequence. Sobol sequences belong to the so-called low-discrepancy sequences, which satisfy the properties of random numbers, but are distributed more evenly...