Derivatives analytics with Python data analysis, models, simulation, calibration and hedging
"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 prog...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Chichester, England :
Wiley
2015.
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| Edición: | 1 |
| Colección: | Wiley finance series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629911406719 |
Tabla de Contenidos:
- Derivatives Analytics with Python; Contents; List of Tables; List of Figures; Preface; 1 A Quick Tour; 1.1 Market-Based Valuation; 1.2 Structure of the Book; 1.3 Why Python?; 1.4 Further Reading; PART ONE The Market; 2 What is Market-Based Valuation?; 2.1 Options and their Value; 2.2 Vanilla vs. Exotic Instruments; 2.3 Risks Affecting Equity Derivatives; 2.3.1 Market Risks; 2.3.2 Other Risks; 2.4 Hedging; 2.5 Market-Based Valuation as a Process; 3 Market Stylized Facts; 3.1 Introduction; 3.2 Volatility, Correlation and Co.; 3.3 Normal Returns as the Benchmark Case; 3.4 Indices and Stocks
- 3.4.1 Stylized Facts3.4.2 DAX Index Returns; 3.5 Option Markets; 3.5.1 Bid/Ask Spreads; 3.5.2 Implied Volatility Surface; 3.6 Short Rates; 3.7 Conclusions; 3.8 Python Scripts; 3.8.1 GBM Analysis; 3.8.2 DAX Analysis; 3.8.3 BSM Implied Volatilities; 3.8.4 EURO STOXX 50 Implied Volatilities; 3.8.5 Euribor Analysis; PART TWO Theoretical Valuation; 4 Risk-Neutral Valuation; 4.1 Introduction; 4.2 Discrete-Time Uncertainty; 4.3 Discrete Market Model; 4.3.1 Primitives; 4.3.2 Basic Definitions; 4.4 Central Results in Discrete Time; 4.5 Continuous-Time Case; 4.6 Conclusions; 4.7 Proofs
- 4.7.1 Proof of Lemma 14.7.2 Proof of Proposition 1; 4.7.3 Proof of Theorem 1; 5 Complete Market Models; 5.1 Introduction; 5.2 Black-Scholes-Merton Model; 5.2.1 Market Model; 5.2.2 The Fundamental PDE; 5.2.3 European Options; 5.3 Greeks in the BSM Model; 5.4 Cox-Ross-Rubinstein Model; 5.5 Conclusions; 5.6 Proofs and Python Scripts; 5.6.1 Itô's Lemma; 5.6.2 Script for BSM Option Valuation; 5.6.3 Script for BSM Call Greeks; 5.6.4 Script for CRR Option Valuation; 6 Fourier-Based Option Pricing; 6.1 Introduction; 6.2 The Pricing Problem; 6.3 Fourier Transforms; 6.4 Fourier-Based Option Pricing
- 6.4.1 Lewis (2001) Approach6.4.2 Carr-Madan (1999) Approach; 6.5 Numerical Evaluation; 6.5.1 Fourier Series; 6.5.2 Fast Fourier Transform; 6.6 Applications; 6.6.1 Black-Scholes-Merton (1973) Model; 6.6.2 Merton (1976) Model; 6.6.3 Discrete Market Model; 6.7 Conclusions; 6.8 Python Scripts; 6.8.1 BSM Call Valuation via Fourier Approach; 6.8.2 Fourier Series; 6.8.3 Roots of Unity; 6.8.4 Convolution; 6.8.5 Module with Parameters; 6.8.6 Call Value by Convolution; 6.8.7 Option Pricing by Convolution; 6.8.8 Option Pricing by DFT; 6.8.9 Speed Test of DFT
- 7 Valuation of American Options by Simulation7.1 Introduction; 7.2 Financial Model; 7.3 American Option Valuation; 7.3.1 Problem Formulations; 7.3.2 Valuation Algorithms; 7.4 Numerical Results; 7.4.1 American Put Option; 7.4.2 American Short Condor Spread; 7.5 Conclusions; 7.6 Python Scripts; 7.6.1 Binomial Valuation; 7.6.2 Monte Carlo Valuation with LSM; 7.6.3 Primal and Dual LSM Algorithms; PART THREE Market-Based Valuation; 8 A First Example of Market-Based Valuation; 8.1 Introduction; 8.2 Market Model; 8.3 Valuation; 8.4 Calibration; 8.5 Simulation; 8.6 Conclusions; 8.7 Python Scripts
- 8.7.1 Valuation by Numerical Integration
