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Listed Volatility and Variance Derivatives

Book Description

Leverage Python for expert-level volatility and variance derivative trading

Listed Volatility and Variance Derivatives is a comprehensive treatment of all aspects of these increasingly popular derivatives products, and has the distinction of being both the first to cover European volatility and variance products provided by Eurex and the first to offer Python code for implementing comprehensive quantitative analyses of these financial products. For those who want to get started right away, the book is accompanied by a dedicated Web page and a Github repository that includes all the code from the book for easy replication and use, as well as a hosted version of all the code for immediate execution.

Python is fast making inroads into financial modelling and derivatives analytics, and recent developments allow Python to be as fast as pure C++ or C while consisting generally of only 10% of the code lines associated with the compiled languages. This complete guide offers rare insight into the use of Python to undertake complex quantitative analyses of listed volatility and variance derivatives.

  • Learn how to use Python for data and financial analysis, and reproduce stylised facts on volatility and variance markets
  • Gain an understanding of the fundamental techniques of modelling volatility and variance and the model-free replication of variance
  • Familiarise yourself with micro structure elements of the markets for listed volatility and variance derivatives
  • Reproduce all results and graphics with IPython/Jupyter Notebooks and Python codes that accompany the book

Listed Volatility and Variance Derivatives is the complete guide to Python-based quantitative analysis of these Eurex derivatives products.

Table of Contents

  1. Preface
  2. Part One: Introduction to Volatility and Variance
    1. Chapter 1: Derivatives, Volatility and Variance
      1. 1.1 Option Pricing and Hedging
      2. 1.2 Notions of Volatility and Variance
      3. 1.3 Listed Volatility and Variance Derivatives
      4. 1.4 Volatility and Variance Trading
      5. 1.5 Python as Our Tool of Choice
      6. 1.6 Quick Guide Through the Rest of the Book
    2. Chapter 2: Introduction to Python
      1. 2.1 Python Basics
      2. 2.2 NumPy
      3. 2.3 matplotlib
      4. 2.4 pandas
      5. 2.5 Conclusions
    3. Chapter 3: Model-Free Replication of Variance
      1. 3.1 Introduction
      2. 3.2 Spanning with Options
      3. 3.3 Log Contracts
      4. 3.4 Static Replication of Realized Variance and Variance Swaps
      5. 3.5 Constant Dollar Gamma Derivatives and Portfolios
      6. 3.6 Practical Replication of Realized Variance
      7. 3.7 VSTOXX as Volatility Index
      8. 3.8 Conclusions
  3. Part Two: Listed Volatility Derivatives
    1. Chapter 4: Data Analysis and Strategies
      1. 4.1 Introduction
      2. 4.2 Retrieving Base Data
      3. 4.3 Basic Data Analysis
      4. 4.4 Correlation Analysis
      5. 4.5 Constant Proportion Investment Strategies
      6. 4.6 Conclusions
    2. Chapter 5: VSTOXX Index
      1. 5.1 Introduction
      2. 5.2 Collecting Option Data
      3. 5.3 Calculating the Sub-Indexes
      4. 5.4 Calculating the VSTOXX Index
      5. 5.5 Conclusions
      6. 5.6 Python Scripts
    3. Chapter 6: Valuing Volatility Derivatives
      1. 6.1 Introduction
      2. 6.2 The Valuation Framework
      3. 6.3 The Futures Pricing Formula
      4. 6.4 The Option Pricing Formula
      5. 6.5 Monte Carlo Simulation
      6. 6.6 Automated Monte Carlo Tests
      7. 6.7 Model Calibration
      8. 6.8 Conclusions
      9. 6.9 Python Scripts
    4. Chapter 7: Advanced Modeling of the VSTOXX Index
      1. 7.1 Introduction
      2. 7.2 Market Quotes for Call Options
      3. 7.3 The SRJD Model
      4. 7.4 Term Structure Calibration
      5. 7.5 Option Valuation by Monte Carlo Simulation
      6. 7.6 Model Calibration
      7. 7.7 Conclusions
      8. 7.8 Python Scripts
    5. Chapter 8: Terms of the VSTOXX and its Derivatives
      1. 8.1 The EURO STOXX 50 Index
      2. 8.2 The VSTOXX Index
      3. 8.3 VSTOXX Futures Contracts
      4. 8.4 VSTOXX Options Contracts
      5. 8.5 Conclusions
  4. Part Three: Listed Variance Derivatives
    1. Chapter 9: Realized Variance and Variance Swaps
      1. 9.1 Introduction
      2. 9.2 Realized Variance
      3. 9.3 Variance Swaps
      4. 9.4 Variance vs. Volatility
      5. 9.5 Conclusions
    2. Chapter 10: Variance Futures at Eurex
      1. 10.1 Introduction
      2. 10.2 Variance Futures Concepts
      3. 10.3 Example Calculation for a Variance Future
      4. 10.4 Comparison of Variance Swap and Future
      5. 10.5 Conclusions
    3. Chapter 11: Trading and Settlement
      1. 11.1 Introduction
      2. 11.2 Overview of Variance Futures Terms
      3. 11.3 Intraday Trading
      4. 11.4 Trade Matching
      5. 11.5 Different Traded Volatilities
      6. 11.6 After the Trade Matching
      7. 11.7 Further Details
      8. 11.8 Conclusions
  5. Part Four: DX Analytics
    1. Chapter 12: DX Analytics – An Overview
      1. 12.1 Introduction
      2. 12.2 Modeling Risk Factors
      3. 12.3 Modeling Derivatives
      4. 12.4 Derivatives Portfolios
      5. 12.5 Conclusions
    2. Chapter 13: DX Analytics – Square-Root Diffusion
      1. 13.1 Introduction
      2. 13.2 Data Import and Selection
      3. 13.3 Modeling the VSTOXX Options
      4. 13.4 Calibration of the VSTOXX Model
      5. 13.5 Conclusions
      6. 13.6 Python Scripts
    3. Chapter 14: DX Analytics – Square-Root Jump Diffusion
      1. 14.1 Introduction
      2. 14.2 Modeling the VSTOXX Options
      3. 14.3 Calibration of the VSTOXX Model
      4. 14.4 Calibration Results
      5. 14.5 Conclusions
      6. 14.6 Python Scripts
  6. Bibliography
  7. Index
  8. EULA