Derivatives Analytics with Python : Data Analysis, Models, Simulation, Calibration and Hedging.
Yves. Hilpisch
Bok Engelsk 2015 · Electronic books.
| Omfang | 1 online resource (377 pages)
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|---|---|
| Utgave | 1st ed.
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| Opplysninger | Intro -- 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 Facts -- 3.4.2 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 1 -- 4.7.2 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.. - 12.5.1 Simulating the BCC97 Model -- 12.5.2 Valuation of European Call Options by MCS -- 12.5.3 Valuation of American Call Options by MCS -- 13 Dynamic Hedging -- 13.1 Introduction -- 13.2 Hedging Study for BSM Model -- 13.3 Hedging Study for BCC97 Model -- 13.4 Conclusions -- 13.5 Python Scripts -- 13.5.1 LSM Delta Hedging in BSM (Single Path) -- 13.5.2 LSM Delta Hedging in BSM (Multiple Paths) -- 13.5.3 LSM Algorithm for American Put in BCC97 -- 13.5.4 LSM Delta Hedging in BCC97 (Single Path) -- 14 Executive Summary -- APPENDIX A: Python in a Nutshell -- A.1 Python Fundamentals -- A.1.1 Installing Python Packages -- A.1.2 First Steps with Python -- A.1.3 Array Operations -- A.1.4 Random Numbers -- A.1.5 Plotting -- A.2 European Option Pricing -- A.2.1 Black-Scholes-Merton Approach -- A.2.2 Cox-Ross-Rubinstein Approach -- A.2.3 Monte Carlo Approach -- A.3 Selected Financial Topics -- A.3.1 Approximation -- A.3.2 Optimization -- A.3.3 Numerical Integration -- A.4 Advanced Python Topics -- A.4.1 Classes and Objects -- A.4.2 Basic Input-Output Operations -- A.4.3 Interacting with Spreadsheets -- A.5 Rapid Financial Engineering -- Bibliography -- Index -- EULA.. - 6.3 Fourier Transforms -- 6.4 Fourier-Based Option Pricing -- 6.4.1 Lewis (2001) Approach -- 6.4.2 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 Simulation -- 7.1 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 -- 8.7.2 Valuation by FFT -- 8.7.3 Calibration to Three Maturities -- 8.7.4 Calibration to Short Maturity -- 8.7.5 Valuation by MCS -- 9 General Model Framework -- 9.1 Introduction -- 9.2 The Framework -- 9.3 Features of the Framework -- 9.4 Zero-Coupon Bond Valuation -- 9.5 European Option Valuation -- 9.5.1 PDE Approach -- 9.5.2 Transform Methods -- 9.5.3 Monte Carlo Simulation -- 9.6 Conclusions -- 9.7 Proofs and Python Scripts -- 9.7.1 Itô's Lemma -- 9.7.2 Python Script for Bond Valuation.. - 9.7.3 Python Script for European Call Valuation -- 10 Monte Carlo Simulation -- 10.1 Introduction -- 10.2 Valuation of Zero-Coupon Bonds -- 10.3 Valuation of European Options -- 10.4 Valuation of American Options -- 10.4.1 Numerical Results -- 10.4.2 Higher Accuracy vs. Lower Speed -- 10.5 Conclusions -- 10.6 Python Scripts -- 10.6.1 General Zero-Coupon Bond Valuation -- 10.6.2 CIR85 Simulation and Valuation -- 10.6.3 Automated Valuation of European Options by Monte Carlo Simulation -- 10.6.4 Automated Valuation of American Put Options by Monte Carlo Simulation -- 11 Model Calibration -- 11.1 Introduction -- 11.2 General Considerations -- 11.2.1 Why Calibration at All? -- 11.2.2 Which Role Do Different Model Components Play? -- 11.2.3 What Objective Function? -- 11.2.4 What Market Data? -- 11.2.5 What Optimization Algorithm? -- 11.3 Calibration of Short Rate Component -- 11.3.1 Theoretical Foundations -- 11.3.2 Calibration to Euribor Rates -- 11.4 Calibration of Equity Component -- 11.4.1 Valuation via Fourier Transform Method -- 11.4.2 Calibration to EURO STOXX 50 Option Quotes -- 11.4.3 Calibration of H93 Model -- 11.4.4 Calibration of Jump Component -- 11.4.5 Complete Calibration of BCC97 Model -- 11.4.6 Calibration to Implied Volatilities -- 11.5 Conclusions -- 11.6 Python Scripts for Cox-Ingersoll-Ross Model -- 11.6.1 Calibration of CIR85 -- 11.6.2 Calibration of H93 Stochastic Volatility Model -- 11.6.3 Comparison of Implied Volatilities -- 11.6.4 Calibration of Jump-Diffusion Part of BCC97 -- 11.6.5 Calibration of Complete Model of BCC97 -- 11.6.6 Calibration of BCC97 Model to Implied Volatilities -- 12 Simulation and Valuation in the General Model Framework -- 12.1 Introduction -- 12.2 Simulation of BCC97 Model -- 12.3 Valuation of Equity Options -- 12.3.1 European Options -- 12.3.2 American Options -- 12.4 Conclusions -- 12.5 Python Scripts.. - 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.
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| Emner | |
| Sjanger | |
| Dewey | |
| ISBN | 9781119037934
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| ISBN(galt) |
