Learn and implement various Quantitative Finance concepts using the
popular Python libraries About This Book * 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 Who This Book Is For
This book assumes that the readers have some basic knowledge
related to Python. However, he/she has no knowledge of quantitative
finance. In addition, he/she has no knowledge about financial data.
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
In Detail 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. Style and approach This book
takes a step-by-step approach in explaining the libraries and
modules in Python, and how they can be used to implement various
aspects of quantitative finance. Each concept is explained in depth
and supplemented with code examples for better understanding.
General
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