Derivatives Analytics with Python

Book Description

Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging (The Wiley Finance Series)

Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You’ll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional’s guide to exploiting Python’s capabilities for efficient and performing derivatives analytics. * Reproduce major stylized facts of equity and options markets yourself * Apply Fourier transform techniques and advanced Monte Carlo pricing * Calibrate advanced option pricing models to market data * Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.

Table of Contents

Chapter 1. A Quick Tour

Part One The Market
Chapter 2. What is Market-Based Valuation?
Chapter 3. Market Stylized Facts

Part Two Theoretical Valuation
Chapter 4. Risk-Neutral Valuation
Chapter 5. Complete Market Models
Chapter 6. Fourier-Based Option Pricing
Chapter 7. Valuation of American Options by Simulation

Part Three Market-Based Valuation
Chapter 8. A First Example of Market-Based Valuation
Chapter 9. General Model Framework
Chapter 10. Monte Carlo Simulation
Chapter 11. Model Calibration
Chapter 12. Simulation and Valuation in the General Model Framework
Chapter 13. Dynamic Hedging
Chapter 14. Executive Summary

中文：

书名：使用Python进行衍生品分析

使用Python的衍生品分析：数据分析、模型、模拟、校准和套期保值(Wiley Finance系列)

超级充值期权分析和套期保值结合使用Python衍生品分析的强大功能向您展示了如何使用先进的金融模型、高效的数值技术和Python编程语言的强大功能来实现市场一致的估值和对冲方法。这本独特的指南提供了所有理论、方法和过程的详细解释，为您提供了从稳健的基础上评估股票指数期权所需的背景和工具。您将找到并使用自含式的Python脚本和模块，并了解如何将Python应用于高级数据和衍生品分析，因为我们提供了5,000多行代码来帮助您重现所显示的结果和图形。覆盖范围包括市场数据分析、风险中性估值、蒙特卡洛模拟、模型校准、估值和动态对冲，以及展示随机波动性、跳跃分量、随机短期利率等的模型。配套网站以所有代码和IPython笔记本为特色，可立即执行和自动化。Python在衍生品分析领域取得了进展，使机构能够快速高效地提供投资组合、交易和风险管理结果。这本书是金融专业人士的指南，以利用Python的能力进行高效和高性能的衍生品分析。*自己复制股票和期权市场的主要风格化事实*应用傅立叶变换技术和先进的蒙特卡罗定价*根据市场数据校准先进的期权定价模型*整合先进的模型和数字方法以动态对冲期权最近的发展使分析师能够像使用C或C++一样执行分析任务，但只使用大约十分之一或更少的代码。衍生品分析与Python数据分析、模型、模拟、校准和套期保值，向您展示了您需要了解的内容，以增强您的衍生品和风险分析工作。

目录表

第1章：快速游览

第一部分：市场
第二章什么是基于市场的估值？
第三章：市场风格化事实

第二部分理论估值
第四章风险中性估值
Chapter 5. Complete Market Models
第六章基于傅里叶变换的期权定价
第七章：美式期权的模拟定价

第三部分市场化评估
第八章基于市场的估值的第一个例子
第9章.通用模型框架
第十章蒙特卡洛模拟
第11章.模型校准
第12章：通用模型框架中的模拟和估值
Chapter 13. Dynamic Hedging
第14章.执行摘要