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Stochastic Finance with Python Design Financial Models from Probabilistic Perspe.pdf
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内容非常精彩，接近400页的大型资料，实用，翔实，全部矢量文字适合投喂给大模型等工具，翻译也极为方便。
Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective
Part I: Foundations ------.1
Chapter 1: Introduction ------... 3
What Is Quantitative Finance ------... 3
Why Stochastic --------. 5
What Is Special About Stochastic Methodologies ----.. 6
Numerical Implementation -------- 9
Why Python -------- 10
The Approach of Pythonic Implementation ----.. 10
Probabilistic and Numerical Programming ----.. 11
Summary --------... 13
Chapter 2: Finance Basics and Data Sources ----... 15
Different Financial Assets ------.. 15
Stocks --------... 15
Options --------. 16
Portfolio -------- 17
Basic Interest Theory --------.. 18
Simple Interest --------.. 19
Discrete Compound Interest ------. 19
Continuous Compound Interest ------ 20
Data Source Adapters for Financial Data ------... 21
Yahoo Financials -------- 22
Market Stack --------.
Returns --------.. 30
Simple Return -------- 31
Multiperiod Simple Returns ------.. 33
Log Returns --------... 34
Multiperiod Log Returns ------... 34
Summary --------... 35
Chapter 3: Probability ------ 37
The Inception of the Idea for Probability Theory ----... 37
Probability Space and Basic Definitions ------ 38
Definition of Probability ------ 39
Why Study Probability for Finance ------ 40
Set-Theoretic View of Probability ------. 41
Random Variable --------. 43
Discrete Random Variable ------ 44
Continuous Random Variable ------ 47
Probability Distributions --------.. 48
Joint and Marginal Distribution ------ 50
Likelihood and Parameters ------... 51
Moments, Expectation, and Variance ------ 52
Poisson Distribution -------- 55
Uniform Distribution -------- 57
Exponential Distribution --------.. 58
Gaussian/Normal Distribution ------ 60
Characteristic Function --------... 62
Parameter Estimation --------. 67
Frequentist Method --------... 68
Method of Moments -------- 79
Bayesian Method --------... 82
Summary --------... 93
Chapter 4: Simulation ------ 95
Random Variable Generation ------.. 95
Inverse Transform Method ------ 96
Inverse Method for PMF ------. 100
Acceptance/Rejection Method ------ 102
Monte Carlo Simulation --------. 115
Variance Reduction --------.. 124
Summary --------. 130
Chapter 5: Stochastic Process ---- 131
Inception of Stochastic Process ------... 131
Random Walk Model --------. 133
Statistical Metrics of Symmetric Random Walk Model ----.. 134
Quadratic Variation of Symmetric Random Walk Model ---- 135
Scaled Random Walk Model ------ 136
Brownian Motion --------... 140
Stochastic Calculus and Integrals – A Brief Introduction --. 141
Stochastic Differential Equation – Financial Asset Dynamics --.. 142
Poisson Process -------- 150
Summary --------. 153
Part II: Basic Asset Price Modeling ----..155
Chapter 6: Diffusion Model ------.. 157
Modeling Financial Asset Price with SDE ------. 157
SDE-Based Model-Building Steps ------.. 158
Formation of SDE – Log-Asset Price and Ito Lemma ----. 159
Risk-Neutral Settings -------- 164
Estimation of PDF and Its Parameters ------ 164
Inference --------.. 172
Monte Carlo Simulation of Diffusion Model ----... 173
Time Unit Transformation ------... 176
Average Forecast – Mean Path ------... 177
Uncertainty Bounds --------. 178
Backtesting and RMSE Score ------. 178
Change of Frequency -------- 180
Computing Distributions of the Mean Path ---- 181
Comparison and Improvement ------. 185
Summary --------. 187
Chapter 7: Jump Models ------.. 189
General Formation of Jump Model ------... 189
Ito Lemma for Jump Model ------ 190
Templates in Python for Parametric Jump-Diffusion Process --.. 192
Merton Model -------- 195
Path Generation for Merton Model ------. 196
Parameter Estimation of Merton Model ----. 198
Forecasting with Merton Model ------.. 213
Kou Model -------- 219
Sampling Jumps from Asymmetric Double Exponential Distribution --... 219
Stochastic Process for Kou Model and Path Generation --.. 221
Parameter Estimation of Kou Model ------... 225
Forecasting with Kou Model ------... 228
Nonparametric Models --------.. 231
Brief Review of the Kernel Method ------. 234
Summary --------. 256
Part III: Financial Options Modeling ----..257
Chapter 8: Options and Black- Scholes Model --.. 259
Options – Basics and Formulations ------. 260
Option Nomenclatures ------ 260
Payoff Function -------- 262
Put-Call Parity --------.. 263
Black-Scholes Model -------- 264
Risk-Neutral Probability Method ------ 265
Summary --------. 308
Chapter 9: PDE, Finite Difference, and Black-Scholes Model -- 309
PDE – A Short Introduction ------... 309
Solution of PDE – Finite Difference Method (FDM) ----. 311
Explicit Method -------- 314
Implicit Method -------- 328
Crank-Nicolson Method ------.. 337
Black-Scholes PDE -------- 343
Implicit FDM for the Black-Scholes Model ---- 345
Integration with Diffusion Model and Python Implementation --. 346
Summary --------. 350
Part IV: Portfolios ------...353
Chapter 10: Portfolio Optimization ----. 355
Brief Idea About Portfolios ------... 355
The Mean-Variance Analysis ------ 356
Portfolio Simulation --------... 363
Minimum Variance Portfolio ------. 369
Additional Constraints ------ 377
Efficient Frontier --------... 381
Efficient Frontier Simulation ------... 382
Summary --------. 385
Bibliography ------ 387
Index --------. 389
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