Additional information
by Yves Hilpisch (Author)
Leverage Python for expert-level volatility and variance derivative trading
Listed Volatility and Variance Derivatives is a comprehensive treatment of all aspects of these increasingly popular derivatives products, and has the distinction of being both the first to cover European volatility and variance products provided by Eurex and the first to offer Python code for implementing comprehensive quantitative analyses of these financial products. For those who want to get started right away, the book is accompanied by a dedicated Web page and a Github repository that includes all the code from the book for easy replication and use, as well as a hosted version of all the code for immediate execution.
Python is fast making inroads into financial modelling and derivatives analytics, and recent developments allow Python to be as fast as pure C++ or C while consisting generally of only 10% of the code lines associated with the compiled languages. This complete guide offers rare insight into the use of Python to undertake complex quantitative analyses of listed volatility and variance derivatives.
- Learn how to use Python for data and financial analysis, and reproduce stylised facts on volatility and variance markets
- Gain an understanding of the fundamental techniques of modelling volatility and variance and the model-free replication of variance
- Familiarise yourself with micro structure elements of the markets for listed volatility and variance derivatives
- Reproduce all results and graphics with IPython/Jupyter Notebooks and Python codes that accompany the book
Listed Volatility and Variance Derivatives is the complete guide to Python-based quantitative analysis of these Eurex derivatives products.
Front Jacket
Python in general requires much less code than other languages, like C++ or C#, to accomplish the same goal. Because of this and also due to its powerful ecosystem of libraries, it has become one of the most widely used programming languages and technology platforms in the financial industry. Listed Volatility and Variance Derivatives is your Python-based A-to-Z guide to the most important listed volatility and variance derivatives provided by Eurex.
This complete guide is the first of its kind to offer practical, expert insight into how industry leaders use Python to undertake complex quantitative analysis in the field. From understanding the fundamental techniques of modeling to reproducing your own results and graphics with Jupyter Notebooks, this single resource gives you everything you need to use this powerful language to support portfolio, trading and risk management functions.
Enhance and streamline your quantitative analysis with Listed Volatility and Variance Derivatives.
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Robust Analytics for Trading Listed Volatility and Variance Derivatives
Whether you're new to programming or want to step up from C++, C# or Matlab, Listed Volatility and Variance Derivatives jumpstarts you on a faster, more powerful way to execute quantitative analysis to trade listed volatility and variance products. No other resource offers indepth coverage on European products provided by Eurex along with step-by-step explanations of the Python codes you need to gain an edge in this competitive space.
Complete with an accompanying website allowing you to download all the code inside, you can easily and immediately execute the covered techniques for:
- Using Python to analyze data and financials and reproduce stylized facts on volatility and variance markets.
- Modeling volatility and variance and replicating variance in a model-free fashion.
- Navigating the micro-structure elements of the markets for listed volatility and variance derivatives.
The Python ecosystem thrives in the most demanding financial environments, and Listed Volatility and Variance Derivatives is the only guidebook for using it to master this analytics space.
Author Biography
DR. YVES HILPISCH is founder and managing partner of The Python Quants (http: //tpq.io), a group focusing on the use of open source technologies for financial data science, algorithmic trading and computational finance. He is the author of Python for Finance, and Derivatives Analytics with Python. Yves lectures on computational finance on the CQF Program as well as on data science at htw saar University of Applied Sciences. He has written the financial analytics library DX Analytics (http: //dx-analytics.com) and organizes meetup groups and conferences about Python for quantitative finance in Frankfurt, London and New York.
