The book starts by reviewing financial concepts, such as analyzing different asset types like sks, options, and portfolios. It then delves into the crux of shastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and shastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - shastic calculus and shastic differential equations (SDE).
Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.
What You Will Learn
• Understand applied probability and statistics with finance
• Design forecasting models of the sk price with the shastic process, Monte-Carlo simulation.
• Option price estimation with both risk-neutral probabilistic and PDE-driven approach.
• Use Object-oriented Python to design financial models with reusability.
Who This Book Is For
Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance
The book starts by reviewing financial concepts, such as analyzing different asset types like sks, options, and portfolios. It then delves into the crux of shastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and shastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - shastic calculus and shastic differential equations (SDE).
Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.
What You Will Learn
• Understand applied probability and statistics with finance
• Design forecasting models of the sk price with the shastic process, Monte-Carlo simulation.
• Option price estimation with both risk-neutral probabilistic and PDE-driven approach.
• Use Object-oriented Python to design financial models with reusability.
Who This Book Is For
Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance
Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective
396
Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective
396Paperback(First Edition)
Product Details
| ISBN-13: | 9798868810510 |
|---|---|
| Publisher: | Apress |
| Publication date: | 12/14/2024 |
| Edition description: | First Edition |
| Pages: | 396 |
| Product dimensions: | 7.01(w) x 10.00(h) x (d) |