Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging

by Yves Hilpisch

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. 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.

Genres: Finance, Programming

384 pages, Hardcover

First published May 4, 2015

Reviews

[Amine · Rating 4 out of 5]

I love this book but it would be much better if the author treated more recent models. So on the bright side, there are very few books out there with practical implementations. It is much effort to code a paper in Python and it is quite incredible that Yves Hilpisch provides this. Really impressive and clear. The book could be challenging for the least mathematical readers -- this is very advanced material, and a gem. The biggest warning for readers is that the models essentially stop at Baksi et al, which was published in 1997. A lot has been done since then and although Yves mentions the work of Duffie et al, he doesn't show the implementation of it. Why? It would make so much sense to add one chapter on Affine Jump Diffusion. This is the frontier today, and it's missing from the book. So unfortunate for an otherwise great book. Final word: I think the book could be clearer on the transformation from physical probability to risk-neutral probability. There is a missing transition there, and I had to look for other sources to get the pdf of the risk neutral probability distribution.

[Yogesh Aggarwal · Rating 5 out of 5]

whole practical elaboration fantastic book with times series methods to know before you implement the book in life. practical with times, jyupiter,pandas,numby, sci, lots more t elaborate with practicals.

[Konstantin Nikitin · Rating 2 out of 5]

Impossible to reads without a strong mathematics baxkground

[Lucas Peres · Rating 3 out of 5]

Pelo fato de ter os códigos fontes ele mereceu as 3 estrelas. Porém, as descrições dos modelos são complicadas demais e assume que o leitor lembre de tópicos não triviais de variáveis complexas e teoria da medida. O que torna a leitura enfadonha e de difícil assimilação.