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Financial Theory with Python

Release Date: 2021/10/01
ISBN: 9781098104344
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Nowadays, finance, mathematics, and programming are intrinsically linked. Financial Theory with Python provides 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, Financial Theory with Python teaches you the basics of financial economics. Written by the bestselling author of Python for Finance, Yves Hilpisch, this practical guide 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
  • Utilize 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

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
  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
    2. Real Assets
    3. Agents
    4. Time
    5. Money
    6. Cash Flow
    7. Return
    8. Interest
    9. Present Value
    10. Net Present Value
    11. Uncertainty
    12. Financial Assets
    13. Risk
    14. Probability Measure
    15. Expectation
    16. Expected Return
    17. Volatility
    18. Contingent Claims
    19. Replication
    20. Arbitrage Pricing
    21. Market Completeness
    22. Arrow-Debreu Securities
    23. Martingale Pricing
    24. First Fundamental Theorem of Asset Pricing
    25. Pricing by Expectation
    26. Second Fundamental Theorem of Asset Pricing
    27. Mean-Variance Portfolios
    28. Conclusions
    29. Further Resources
  4. 3. Three State Economy
    1. Uncertainty
    2. Financial Assets
    3. Attainable Contingent Claims
    4. Martingale Pricing
    5. Martingale Measures
    6. Risk-Neutral Pricing
    7. Super-Replication
    8. Approximate Replication
    9. Capital Market Line
    10. Capital Asset Pricing Model
    11. Conclusions
    12. Further Resources
  5. 4. Optimality and Equilibrium
    1. Utility Maximization
    2. Indifference Curves
    3. Appropriate Utility Functions
    4. Logarithmic Utility
    5. Time-Additive Utility
    6. Expected Utility
    7. Optimal Investment Portfolio
    8. Time-Additive Expected Utility
    9. Pricing in Complete Markets
    10. Arbitrage Pricing
    11. Martingale Pricing
    12. Risk-Less Interest Rate
    13. A Numerical Example (I)
    14. Pricing in Incomplete Markets
    15. Martingale Measures
    16. Equilibrium Pricing
    17. A Numerical Example (II)
    18. Conclusions
    19. Further Resources
  6. 5. Static Economy
    1. Uncertainty
    2. Random Variables
    3. Numerical Examples
    4. Financial Assets
    5. Contingent Claims
    6. Market Completeness
    7. Fundamental Theorems of Asset Pricing
    8. Black-Scholes-Merton Option Pricing
    9. Completeness of Black-Scholes-Merton
    10. Merton Jump-Diffusion Option Pricing
    11. Representative Agent Pricing
    12. Conclusions
    13. Further Resources
  7. 6. Dynamic Economy
    1. Binomial Option Pricing
    2. Simulation & Valuation Based on Python Loops
    3. Simulation & Valuation Based on Vectorized Code
    4. Speed Comparison
    5. Black-Scholes-Merton Option Pricing
    6. Monte Carlo Simulation of Stock Price Paths
    7. Monte Carlo Valuation of the European Put Option
    8. Monte Carlo Valuation of the American Put Option
    9. Conclusions
    10. Further Resources
  8. 7. Where to Go From Here?
    1. Mathematics
    2. Financial Theory
    3. Python Programming
    4. Python for Finance
    5. Financial Data Science
    6. Algorithmic Trading
    7. Computational Finance
    8. Artificial Intelligence
    9. Other Resources
    10. Final Words
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