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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

- Preface
- 1. Finance and Python
- 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
- 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 & Valuation Based on Python Loops
- Simulation & 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?