دانلود کتاب Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective

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Table of Contents
About the Author
About the Technical Reviewer
Introduction
Chapter 1: Introduction
What Is Quantitative Finance
Why Stochastic
What Is Special About Stochastic Methodologies
Numerical Implementation
Why Python
The Approach of Pythonic Implementation
Probabilistic and Numerical Programming
Summary
Chapter 2: Finance Basics and Data Sources
Different Financial Assets
Stocks
Options
Portfolio
Basic Interest Theory
Simple Interest
Discrete Compound Interest
Continuous Compound Interest
Data Source Adapters for Financial Data
Yahoo Financials
Market Stack
Returns
Simple Return
Multiperiod Simple Returns
Log Returns
Multiperiod Log Returns
Summary
Chapter 3: Probability
The Inception of the Idea for Probability Theory
Probability Space and Basic Definitions
Definition of Probability
Why Study Probability for Finance
Set-Theoretic View of Probability
Probability Space
Independence and Conditional Probability
Random Variable
Discrete Random Variable
Continuous Random Variable
Probability Distributions
Joint and Marginal Distribution
Likelihood and Parameters
Moments, Expectation, and Variance
Variance
Moments
Moments Approximation
Poisson Distribution
Uniform Distribution
Exponential Distribution
Gaussian/Normal Distribution
Characteristic Function
Parameter Estimation
Frequentist Method
Maximum Likelihood Estimation (MLE)
Log-Likelihood Function of Exponential and Gaussian Distribution
Method of Moments
Bayesian Method
Bayes’ Theorem
Parameter Estimation of Gaussian Distribution with Gaussian Prior
Summary
Chapter 4: Simulation
Random Variable Generation
Inverse Transform Method
Change of Measure
Inverse Method for PMF
Acceptance/Rejection Method
Monte Carlo Simulation
Variance Reduction
Antithetic Sampling
Importance Sampling
Summary
Chapter 5: Stochastic Process
Inception of Stochastic Process
Random Walk Model
Statistical Metrics of Symmetric Random Walk Model
Quadratic Variation of Symmetric Random Walk Model
Scaled Random Walk Model
Brownian Motion
Stochastic Calculus and Integrals – A Brief Introduction
Stochastic Differential Equation – Financial Asset Dynamics
Euler’s Method for Approximating SDE
Basic Forecasting Theory and Monte Carlo Simulation
Poisson Process
Summary
Chapter 6: Diffusion Model
Modeling Financial Asset Price with SDE
SDE-Based Model-Building Steps
Formation of SDE – Log-Asset Price and Ito Lemma
Ito Lemma
Geometric Brownian Motion (GBM) Process and Euler Approximation
Risk-Neutral Settings
Estimation of PDF and Its Parameters
Estimation of Parameters – Likelihood Function and MLE
Numerical Estimation Under the Risk-Neutral Measure
Closed-Form Estimation Under the Real-World Measure
Inference
Monte Carlo Simulation of Diffusion Model
Time Unit Transformation
Average Forecast – Mean Path
Uncertainty Bounds
Backtesting and RMSE Score
Change of Frequency
Computing Distributions of the Mean Path
Comparison and Improvement
Summary
Chapter 7: Jump Models
General Formation of Jump Model
Ito Lemma for Jump Model
Templates in Python for Parametric Jump-Diffusion Process
Characteristic Function of Jump-Diffusion Model
Merton Model
Path Generation for Merton Model
Parameter Estimation of Merton Model
Density Recovery with Fourier Transform
Recovery by FFT Method
Recovery by COS Method
Forecasting with Merton Model
Kou Model
Sampling Jumps from Asymmetric Double Exponential Distribution
Stochastic Process for Kou Model and Path Generation
Parameter Estimation of Kou Model
Forecasting with Kou Model
Methods to Improve the Result
Nonparametric Models
Brief Review of the Kernel Method
Parameter Estimation
Stochastic Process with Gaussian Jumps and Path Simulation
Strategy for Selecting h and σY2(Y)
Estimating h and σY2(Y)
Summary
Chapter 8: Options and Black- Scholes Model
Options – Basics and Formulations
Option Nomenclatures
Payoff Function
Put-Call Parity
Black-Scholes Model
Risk-Neutral Probability Method
Greeks
Delta (∆)
Gamma (Γ)
Theta (Θ)
Vega (Κ)
Rho (Ρ)
Summary
Chapter 9: PDE, Finite Difference, and Black-Scholes Model
PDE – A Short Introduction
Solution of PDE – Finite Difference Method (FDM)
Explicit Method
Python to Implement Explicit Method
Stability Analysis
Implicit Method
Python to Implement Implicit Method
Stability Analysis
Crank-Nicolson Method
Stability Analysis
Black-Scholes PDE
Implicit FDM for the Black-Scholes Model
Integration with Diffusion Model and Python Implementation
Summary
Chapter 10: Portfolio Optimization
Brief Idea About Portfolios
The Mean-Variance Analysis
Portfolio Simulation
Minimum Variance Portfolio
Additional Constraints
Efficient Frontier
Efficient Frontier Simulation
Summary