Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging

By:

Hilpisch, Yves J

Material type: Text

TextPublication details: U K Wiley 2015Description: xvi, 356 pISBN:

978-1119037996

Subject(s):

Hedging (Finance) Derivative securities Python (Computer program language)

DDC classification:

332.645 HIL

Online resources:

Click here to access online

Contents:

Part I: The Market Part II: Theoretical Valuation Part III: Market-Based Valuation

Summary: "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"--

Tags from this library: No tags from this library for this title. Log in to add tags.

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	H.T. Parekh Library	GSB Collection	332.645 HIL (Browse shelf(Opens below))	Available		B2313

Browsing H.T. Parekh Library shelves, Collection: GSB Collection Close shelf browser (Hides shelf browser)

Previous								Next
Previous	332.645 FUS Implementing models in quantitative finance : methods and cases	332.645 GLE Implementing derivatives models	332.645 GRE xVA challenge :	332.645 HIL Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging	332.645 HUN Financial derivatives in theory and practice	332.645 JAC Advanced modelling in finance using Excel and VBA	332.645 JOH Introduction to derivatives : options futures and swaps	Next

Alpha Books 2383/ 02-02-17 Rs.7040/-

Part I: The Market
Part II: Theoretical Valuation
Part III: Market-Based Valuation

"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"--

There are no comments on this title.

to post a comment.