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Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging / Yves Hilpisch
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Wiley finance seriesPublisher: Chichester, West Sussex, United Kingdom : Wiley, 2015Copyright date: ©2015Description: 1 Online-Ressource (XVII, 356 Seiten)ISBN:- 9781119038016
- 9781119038009
- 9781119037934
- 332.64/5702855133
- HG6024.A3
Contents:
Summary: 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.PPN: PPN: 822132966Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PBE | ZDB-30-PQE
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
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