Financial Theory with Python

Financial Theory with Python

by Yves Hilpisch
Released September 2021
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098104351

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Book description

Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance.

Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other.

  • Draw upon mathematics to learn the foundations of financial theory and Python programming
  • Learn about financial theory, financial data modeling, and the use of Python for computational finance
  • Leverage simple economic models to better understand basic notions of finance and Python programming concepts
  • Use both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocation
  • Learn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy

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Table of contents

  1. Preface
    1. Why This Book?
    2. Target Audience
    3. Overview of the Book
    4. Conventions Used in This Book
    5. Using Code Examples
    6. O’Reilly Online Learning
    7. How to Contact Us
    8. Acknowledgments
  2. 1. Finance and Python
    1. A Brief History of Finance
    2. Major Trends in Finance
    3. A Four-Languages World
    4. The Approach of This Book
    5. Getting Started with Python
    6. Conclusions
    7. References
  3. 2. Two-State Economy
    1. Economy
      1. Real Assets
      2. Agents
      3. Time
      4. Money
    2. Cash Flow
      1. Return
      2. Interest
      3. Present Value
      4. Net Present Value
    3. Uncertainty
    4. Financial Assets
    5. Risk
      1. Probability Measure
      2. Expectation
      3. Expected Return
      4. Volatility
    6. Contingent Claims
      1. Replication
      2. Arbitrage Pricing
      3. Market Completeness
    7. Arrow-Debreu Securities
    8. Martingale Pricing
      1. First Fundamental Theorem of Asset Pricing
      2. Pricing by Expectation
      3. Second Fundamental Theorem of Asset Pricing
    9. Mean-Variance Portfolios
    10. Conclusions
    11. Further Resources
  4. 3. Three-State Economy
    1. Uncertainty
    2. Financial Assets
    3. Attainable Contingent Claims
    4. Martingale Pricing
      1. Martingale Measures
      2. Risk-Neutral Pricing
    5. Super-Replication
    6. Approximate Replication
    7. Capital Market Line
    8. Capital Asset Pricing Model
    9. Conclusions
    10. Further Resources
  5. 4. Optimality and Equilibrium
    1. Utility Maximization
      1. Indifference Curves
      2. Appropriate Utility Functions
      3. Logarithmic Utility
      4. Time-Additive Utility
    2. Expected Utility
      1. Optimal Investment Portfolio
      2. Time-Additive Expected Utility
    3. Pricing in Complete Markets
      1. Arbitrage Pricing
      2. Martingale Pricing
    4. Risk-Less Interest Rate
    5. A Numerical Example (I)
    6. Pricing in Incomplete Markets
      1. Martingale Measures
      2. Equilibrium Pricing
    7. A Numerical Example (II)
    8. Conclusions
    9. Further Resources
  6. 5. Static Economy
    1. Uncertainty
      1. Random Variables
      2. Numerical Examples
    2. Financial Assets
    3. Contingent Claims
    4. Market Completeness
    5. Fundamental Theorems of Asset Pricing
    6. Black-Scholes-Merton Option Pricing
    7. Completeness of Black-Scholes-Merton
    8. Merton Jump-Diffusion Option Pricing
    9. Representative Agent Pricing
    10. Conclusions
    11. Further Resources
  7. 6. Dynamic Economy
    1. Binomial Option Pricing
      1. Simulation and Valuation Based on Python Loops
      2. Simulation and Valuation Based on Vectorized Code
      3. Speed Comparison
    2. Black-Scholes-Merton Option Pricing
      1. Monte Carlo Simulation of Stock Price Paths
      2. Monte Carlo Valuation of the European Put Option
      3. Monte Carlo Valuation of the American Put Option
    3. Conclusions
    4. Further Resources
  8. 7. Where to Go from Here?
    1. Mathematics
    2. Financial Theory
    3. Python Programming
    4. Python for Finance
      1. Financial Data Science
      2. Algorithmic Trading
      3. Computational Finance
      4. Artificial Intelligence
      5. Other Resources
    5. Final Words
  9. Index

Product information

  • Title: Financial Theory with Python
  • Author(s): Yves Hilpisch
  • Release date: September 2021
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098104351