Hilpisch - Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging
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This edition first published 2015
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Library of Congress Cataloging-in-Publication Data
Hilpisch, Yves J.
Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging / Yves Hilpisch.1
pages cm.(The Wiley finance series)
Includes bibliographical references and index.
ISBN 978-1-119-03799-6 (hardback)
1. Derivative securities. 2. Hedging (Finance) 3. Python (Computer program language)I. Title.
HG6024.A3H56 2015
332.64'5702855133dc23
2015010191
A catalogue record for this book is available from the British Library.
ISBN 978-1-119-03799-6 (hardback) ISBN 978-1-119-03793-4 (ebk)
ISBN 978-1-119-03800-9 (ebk) ISBN 978-1-119-03801-6 (obk)
Cover Design: Wiley
Cover Images: Top image (c)iStock.com/agsandrew; Bottom image (c)iStock.com/stocksnapper
This book is an outgrowth of diverse activities of myself and colleagues of mine in the fields of financial engineering, computational finance and Python programming at our company The Python Quants GmbH on the one hand and of teaching mathematical finance at Saarland University on the other hand.
The book is targeted at practitioners, researchers and students interested in the market-based valuation of options from a practical perspective, i.e. the single numerical and technical implementation steps that make up such an effort. It is also for those who want to learn how Python can be used for derivatives analytics and financial engineering. However, apart from being primarily practical and implementation-oriented, the book also provides the necessary theoretical foundations and numerical tools.
My hope is that the book will contribute to the increasing acceptance of Python in the financial community, and in particular in the analytics space. If you are interested in getting the Python scripts and IPython Notebooks accompanying the book, you should visit http://wiley.quant-platform.com where you can register for the Quant Platform which allows browser-based, interactive and collaborative financial analytics. Further resources are found on the website http://derivatives-analytics-with-python.com. You should also check out the open source library DX Analytics under http://dx-analytics.com which implements the concepts and methods presented in the book in standardized, reusable fashion.
I thank my familyand in particular my wife Sandrafor their support and understanding that such a project requires many hours of solitude. I also want to thank my colleague Michael Schwed for his continuous help and support. In addition, I thank Alain Ledon and Riaz Ahmad for their comments and feedback. Discussions with participants of seminars and my lectures at Saarland University also helped the project significantly. Parts of this book have benefited from talks I have given at diverse Python and finance conferences over the years.
I dedicate this book to my lovely son Henry Nikolaus whose direct approach to living and clear view of the world I admire.
YVES HILPISCH
Saarland, February 2015
A Quick Tour
This book is about the market-based valuation of (stock) index options. In the domain of derivatives analytics this is an important task which every major investment bank and buy-side decision maker in the financial market is concerned with on a daily basis. While theoretical valuation approaches develop a model, parametrize it and then derive values for options, the market-based approach works the other way round. Prices from liquidly traded options are taken as given (i.e. they are inputs instead of outputs) and one tries to parametrize a market model in a way that replicates the observed option prices as well as possible. This activity is generally referred to as model calibration. Being equipped with a calibrated model, one then proceeds with the task at hand, be it valuation, trading, investing, hedging or risk management. A bit more specifically, one might be interested in pricing and hedging an exotic derivative instrument with such a modelhoping that the results are in line with the overall market (i.e. arbitrage-free and even fair) due to the previous calibration to more simple, vanilla instruments.
To accomplish a market-based valuation, four areas have to be covered:
- market: knowledge about market realities is a conditio sine qua non for any sincere attempt to develop market-consistent models and to accomplish market-based valuation
- theory: every valuation must be grounded on a sound market model, ensuring, for example, the absence of arbitrage opportunities and providing means to derive option values from observed quantities
- numerics: one cannot hope to work with analytical results only; numerical techniques, like Monte Carlo simulation, are generally required in different steps of a market-based valuation process
- technology: to implement numerical techniques efficiently, one is dependent on appropriate technology (hard- and software)
This book covers all of these areas in an integrated manner. It uses equity index options as the prime example for derivative instruments throughout. This, among others, allows to abstract from dividend related issues.
The book is divided into three parts. The first part is concerned with market-based valuation as a process and empirical findings about market realities. The second part covers a number of topics for the theoretical valuation of options and derivatives. It also develops tools much needed during a market-based valuation. The third part finally covers the major aspects related to a market-based valuation and also hedging strategies in such a context.
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