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  • Book Overview & Buying Python for Finance
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Python for Finance

Python for Finance - Second Edition

By : Yuxing Yan
3.4 (32)
Buy this Book
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Python for Finance

Python for Finance

3.4 (32)
By: Yuxing Yan
Buy this Book

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 (17 chapters)
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Preface
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Preface
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A few words for the second edition
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Why Python?
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A programming book written by a finance professor
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What this book covers
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Small-program oriented
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Using real-world data
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Python Basics
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1. Python Basics
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Python installation
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Variable assignment, empty space, and writing our own programs
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Writing a Python function
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Python loops
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Data input
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Data manipulation
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Data output
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Exercises
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Summary
2
2. Introduction to Python Modules
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2. Introduction to Python Modules
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What is a Python module?
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Introduction to NumPy
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Introduction to SciPy
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Introduction to matplotlib
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Introduction to statsmodels
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Introduction to pandas
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Python modules related to finance
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Introduction to the pandas_reader module
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Two financial calculators
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How to install a Python module
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Module dependency
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Exercises
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Summary
3
3. Time Value of Money
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3. Time Value of Money
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Introduction to time value of money
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Writing a financial calculator in Python
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Definition of NPV and NPV rule
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Definition of IRR and IRR rule
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Definition of payback period and payback period rule
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Writing your own financial calculator in Python
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Two general formulae for many functions
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Exercises
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Summary
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4. Sources of Data
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4. Sources of Data
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Diving into deeper concepts
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5. Bond and Stock Valuation
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5. Bond and Stock Valuation
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Introduction to interest rates
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Term structure of interest rates
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Bond evaluation
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Stock valuation
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A new data type – dictionary
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Summary
6
6. Capital Asset Pricing Model
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6. Capital Asset Pricing Model
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Introduction to CAPM
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Moving beta
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Adjusted beta
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Extracting output data
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Simple string manipulation
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Python via Canopy
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References
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Exercises
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Summary
7
7. Multifactor Models and Performance Measures
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7. Multifactor Models and Performance Measures
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Introduction to the Fama-French three-factor model
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Fama-French three-factor model
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Fama-French-Carhart four-factor model and Fama-French five-factor model
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Implementation of Dimson (1979) adjustment for beta
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Performance measures
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How to merge different datasets
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References
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Exercises
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Summary
8
8. Time-Series Analysis
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8. Time-Series Analysis
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Introduction to time-series analysis
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Merging datasets based on a date variable
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Understanding the interpolation technique
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Tests of normality
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52-week high and low trading strategy
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Estimating Roll's spread
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Estimating Amihud's illiquidity
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Estimating Pastor and Stambaugh (2003) liquidity measure
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Fama-MacBeth regression
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Durbin-Watson
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Python for high-frequency data
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Spread estimated based on high-frequency data
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Introduction to CRSP
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References
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Exercises
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Summary
9
9. Portfolio Theory
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9. Portfolio Theory
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Introduction to portfolio theory
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A 2-stock portfolio
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Optimization – minimization
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Forming an n-stock portfolio
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Constructing an optimal portfolio
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Constructing an efficient frontier with n stocks
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References
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Exercises
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Summary
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10. Options and Futures
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10. Options and Futures
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Introducing futures
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Payoff and profit/loss functions for call and put options
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European versus American options
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Black-Scholes-Merton option model on non-dividend paying stocks
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Generating our own module p4f
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European options with known dividends
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Various trading strategies
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Put-call parity and its graphic presentation
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Binomial tree and its graphic presentation
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Hedging strategies
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Implied volatility
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Binary-search
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Retrieving option data from Yahoo! Finance
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Volatility smile and skewness
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References
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Exercises
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Summary
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11. Value at Risk
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11. Value at Risk
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Introduction to VaR
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Normality tests
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Skewness and kurtosis
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Modified VaR
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VaR based on sorted historical returns
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Simulation and VaR
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VaR for portfolios
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Backtesting and stress testing
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Expected shortfall
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References
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Exercises
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Summary
12
12. Monte Carlo Simulation
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12. Monte Carlo Simulation
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Importance of Monte Carlo Simulation
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Generating random numbers from a standard normal distribution
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Generating random numbers with a seed
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Generating random numbers from a uniform distribution
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Using simulation to estimate the pi value
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Generating random numbers from a Poisson distribution
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Selecting m stocks randomly from n given stocks
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With/without replacements
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Distribution of annual returns
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Simulation of stock price movements
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Graphical presentation of stock prices at options' maturity dates
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Replicating a Black-Scholes-Merton call using simulation
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Liking two methods for VaR using simulation
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Capital budgeting with Monte Carlo Simulation
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Python SimPy module
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Comparison between two social policies – basic income and basic job
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Finding an efficient frontier based on two stocks by using simulation
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Constructing an efficient frontier with n stocks
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Long-term return forecasting
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Efficiency, Quasi-Monte Carlo, and Sobol sequences
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References
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Exercises
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Summary
13
13. Credit Risk Analysis
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13. Credit Risk Analysis
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Introduction to credit risk analysis
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Credit rating
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Credit spread
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YIELD of AAA-rated bond, Altman Z-score
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Using the KMV model to estimate the market value of total assets and its volatility
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Term structure of interest rate
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Distance to default
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Credit default swap
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References
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Exercises
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Summary
14
14. Exotic Options
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14. Exotic Options
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European, American, and Bermuda options
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Chooser options
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Shout options
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Binary options
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Rainbow options
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Pricing average options
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Pricing barrier options
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Barrier in-and-out parity
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Graph of up-and-out and up-and-in parity
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Pricing lookback options with floating strikes
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References
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Exercises
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Summary
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15. Volatility, Implied Volatility, ARCH, and GARCH
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15. Volatility, Implied Volatility, ARCH, and GARCH
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Conventional volatility measure – standard deviation
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Tests of normality
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Estimating fat tails
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Lower partial standard deviation and Sortino ratio
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Test of equivalency of volatility over two periods
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Test of heteroskedasticity, Breusch, and Pagan
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Volatility smile and skewness
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Graphical presentation of volatility clustering
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The ARCH model
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Simulating an ARCH (1) process
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The GARCH model
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Simulating a GARCH process
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Simulating a GARCH (p,q) process using modified garchSim()
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GJR_GARCH by Glosten, Jagannanthan, and Runkle
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References
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Exercises
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Summary
16
Index
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Index

Implied volatility

From the previous sections, we know that for a set of input variables—S (the present stock price), X (the exercise price), T (the maturity date in years), r (the continuously compounded risk-free rate), and sigma (the volatility of the stock, that is, the annualized standard deviation of its returns)—we could estimate the price of a call option based on the Black-Scholes-Merton option model. Recall that to price a European call option, we have the following Python code of five lines:

def bs_call(S,X,T,r,sigma):
    from scipy import log,exp,sqrt,stats
d1=(log(S/X)+(r+sigma*sigma/2.)*T)/(sigma*sqrt(T))
d2 = d1-sigma*sqrt(T)
return S*stats.norm.cdf(d1)-X*exp(-r*T)*stats.norm.cdf(d2)

After entering a set of five values, we can estimate the call price as follows:

>>>bs_call(40,40,0.5,0.05,0.25)
3.3040017284767735

On the other hand, if we know S, X, T, r, and c, how can we estimate sigma? Here, sigma is our implied volatility. In other words, if we are given...

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