Learn and implement various Quantitative Finance concepts using the popular Python libraries
Key Features
- Understand the fundamentals of Python data structures and work with time-series data
- Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib
- A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance
Book Description
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.
What you will learn
- Become acquainted with Python in the first two chapters
- Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models
- Learn how to price a call, put, and several exotic options
- Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options
- Understand the concept of volatility and how to test the hypothesis that volatility changes over the years
- Understand the ARCH and GARCH processes and how to write related Python programs
Table of Contents
- Python Basics
- Introduction to Python Modules
- Time Value of Money
- Sources of Data
- Bond and Stock Valuation
- Capital Asset Pricing Model
- Multifactor Models and Performance Measures
- Time-Series Analysis
- Portfolio Theory
- Options and Futures
- Value at Risk
- Monte Carlo Simulation
- Credit Risk Analysis
- Exotic Options
- Volatility, Implied Volatility, ARCH, and GARCH
Dr. Yuxing Yan graduated from McGill University with a PhD in finance. Over the years, he has been teaching various finance courses at eight universities: McGill University and Wilfrid Laurier University (in Canada), Nanyang Technological University (in Singapore), Loyola University of Maryland, UMUC, Hofstra University, University at Buffalo, and Canisius College (in the US).
His research and teaching areas include: market microstructure, open-source finance and financial data analytics. He has 22 publications including papers published in the Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research.
He is good at several computer languages, such as SAS, R, Python, Matlab, and C.
His four books are related to applying two pieces of open-source software to finance: Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016).
In addition, he is an expert on data, especially on financial databases. From 2003 to 2010, he worked at Wharton School as a consultant, helping researchers with their programs and data issues. In 2007, he published a book titled Financial Databases (with S.W. Zhu). This book is written in Chinese.
Currently, he is writing a new book called Financial Modeling Using Excel — in an R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from other similar books related to Excel and financial modeling. New features include using a huge amount of public data related to economics, finance, and accounting; an efficient way to retrieve data: 3 seconds for each time series; a free financial calculator, showing 50 financial formulas instantly, 300 websites, 100 YouTube videos, 80 references, paperless for homework, midterms, and final exams; easy to extend for instructors; and especially, no need to learn R.
