Financial Theory with Python: A Gentle Introduction
- Length: 204 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2021-10-19
- ISBN-10: 1098104358
- ISBN-13: 9781098104351
- Sales Rank: #1253957 (See Top 100 Books)
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
Preface Why This Book? Target Audience Overview of the Book Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. Finance and Python A Brief History of Finance Major Trends in Finance A Four-Languages World The Approach of This Book Getting Started with Python Conclusions References 2. Two-State Economy Economy Real Assets Agents Time Money Cash Flow Return Interest Present Value Net Present Value Uncertainty Financial Assets Risk Probability Measure Expectation Expected Return Volatility Contingent Claims Replication Arbitrage Pricing Market Completeness Arrow-Debreu Securities Martingale Pricing First Fundamental Theorem of Asset Pricing Pricing by Expectation Second Fundamental Theorem of Asset Pricing Mean-Variance Portfolios Conclusions Further Resources 3. Three-State Economy Uncertainty Financial Assets Attainable Contingent Claims Martingale Pricing Martingale Measures Risk-Neutral Pricing Super-Replication Approximate Replication Capital Market Line Capital Asset Pricing Model Conclusions Further Resources 4. Optimality and Equilibrium Utility Maximization Indifference Curves Appropriate Utility Functions Logarithmic Utility Time-Additive Utility Expected Utility Optimal Investment Portfolio Time-Additive Expected Utility Pricing in Complete Markets Arbitrage Pricing Martingale Pricing Risk-Less Interest Rate A Numerical Example (I) Pricing in Incomplete Markets Martingale Measures Equilibrium Pricing A Numerical Example (II) Conclusions Further Resources 5. Static Economy Uncertainty Random Variables Numerical Examples Financial Assets Contingent Claims Market Completeness Fundamental Theorems of Asset Pricing Black-Scholes-Merton Option Pricing Completeness of Black-Scholes-Merton Merton Jump-Diffusion Option Pricing Representative Agent Pricing Conclusions Further Resources 6. Dynamic Economy Binomial Option Pricing Simulation and Valuation Based on Python Loops Simulation and Valuation Based on Vectorized Code Speed Comparison Black-Scholes-Merton Option Pricing Monte Carlo Simulation of Stock Price Paths Monte Carlo Valuation of the European Put Option Monte Carlo Valuation of the American Put Option Conclusions Further Resources 7. Where to Go from Here? Mathematics Financial Theory Python Programming Python for Finance Financial Data Science Algorithmic Trading Computational Finance Artificial Intelligence Other Resources Final Words Index
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