Listed Volatility and Variance Derivatives
A Python-based Guide
What others say.
REVIEWS & Academia
“I’ve had many discussions with Yves and followed his trajectory over the past few years. He is basically a full time professor with a side job as a leading expert in Python for quantitative finance who travels around the world consulting WS and educationally paving the way for any kid who dreams of not just being a Quant, but a new, cutting-edge type of one. For myself having spent most of my career in volatility, he is an invaluable resource. Be warned though this is the third book in his Python quant collection and for most people they’ll be better off starting with his first two works.”
“Volatility derivatives are a class of derivative securities where the payoff explicitly depends on some measure of the volatility of an underlying asset. Prominent examples of these derivatives include variance swaps and VIX futures and options” Peter Carr and Roger Lee (2009) — Volatility Derivatives, Annual Review of Financial Economics.
About the author
Yves Hilpisch is founder and CEO of The Python Quants (cf. https://home.tpq.io) and The AI Machine. The group focuses on Open Source technologies for Financial Data Science, Algorithmic Trading and Computational Finance. It also provides data, financial and derivatives analytics software (cf. Quant Platform and DX Analytics) as well as consulting services and Python for Finance trainings.
Yves is also author of the books Artificial Intelligence in Finance (O’Reilly, 2020), Python for Finance — Mastering Data-Driven Finance (2nd ed., O’Reilly, 2018) and Derivatives Analytics with Python (Wiley, 2015).
As a graduate in Business Administration with a Dr.rer.pol. in Mathematical Finance, he lectures on Computational Finance at the CQF Program and is Adjunct Professor of Computational Finance at Miami Business School. Yves is also the director of the first University Certificate Programs in Python for Algorithmic Trading and Computational Finance.
Furthermore, Yves organizes Python & Open Source for Quant Finance meetups and events in Frankfurt (cf. Open Source in Quant Finance), London (cf. Python for Quant Finance) and New York (cf. For Python Quants).
DX Analytics is a purely Python-based derivatives and risk analytics library which implements all models and approaches presented in the book (e.g. stochastic volatility & jump-diffusion models, Fourier-based option pricing, least-squares Monte Carlo simulation, numerical Greeks) on the basis of a unified API.
We are offering Python for Finance online training classes — leading to a University Certification — about Financial Data Science, Algorithmic Trading and Computational Finance. In addition, we also offer customized corporate training classes. See https://home.tpq.io/training or just get in touch below.
The Experts in Data-Driven and AI-First Finance with Python. We focus on Python and Open Source Technologies for Financial Data Science, Artificial Intelligence, Algorithmic Trading and Computational Finance.