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

Chapter 8. Time-Series Analysis

In finance and economics, a huge amount of our data is in the format of time-series, such as stock prices and Gross Domestic Products (GDP). From Chapter 4, Sources of Data, it is shown that from Yahoo!Finance, we could download daily, weekly, and monthly historical price time-series. From Federal Reserve Bank's Economics Data Library (FRED), we could retrieve many historical time-series such as GDP. For time-series, there exist many issues, such as how to estimate returns from historical price data, how to merge datasets with the same or different frequencies, seasonality, and detect auto-correlation. Understanding those properties is vitally important for our knowledge development.

In this chapter, the following topics will be covered:

  • Introduction to time-series analysis

  • Design a good date variable, and merging different datasets by date

  • Normal distribution and normality test

  • Term structure of interest rates, 52-week high, and low trading strategy

  • Return estimation...