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Using Options Data and Python to Study Equity Market Risk

Article Quant Q&A · Author: Benj Cabalona Jr.
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Summary

The response proposes using programming and financial data skills to study market risk through equity index options. It describes an approach associated with Ian Martin: use prices from a small portfolio of European options on the S&P 500 to infer the market-implied probability of a sufficiently large index decline by expiration. The resulting SVIX measure is presented as a modified version of VIX and can be computed as a weighted average of selected options.

For learning the implementation, the answer points to Python resources on option pricing and finance, which explain formulas and provide working examples. It also mentions a MATLAB toolbox for pricing European options under GARCH-based models, while noting that it supplies pricing functions rather than estimation functions. These are educational leads, not a tested trading strategy: the response gives no performance evidence, and the proposed probability measure is model- and options-market-based rather than a guaranteed forecast.

Key ideas

  • European index option prices can be combined to estimate risk-neutral probabilities of large market declines.
  • The SVIX measure is described as a modified VIX computed from a weighted set of options.
  • Python option-pricing resources can help connect financial formulas with implementation.
  • A mentioned GARCH option-pricing toolbox provides pricing functions but not estimation functions.
  • The response offers learning paths, not evidence of a profitable trading strategy.

Tags

OptionsEquitiesVolatilityDerivatives pricing
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# How Can I Use Python and Data Science in my Personal Trading?


# How Can I Use Python and Data Science in my Personal Trading?












I started learning about the stock market during the Pandemic. I don't necessarily think to be a quant but I'd like to atleast use my skills in programming in python, data visualization, (i don't think machine learning).

Is there like some guides or lectures that shows how i can leverage the above mentioned skills?

Thanks

## Answer by Stéphane (score 2, accepted)

https://quant.stackexchange.com/a/53341

Well, if you are willing to learn a little bit about option pricing, you could take advantage of the large cross-section of derivatives written on some equity securities, such as the stock market index, to inform your bets. In particular, Ian Martin proposed a way to relate the price of a small portfolio of European options on the S&P500 to the probability of observing a fall of at least a certain size in the S&P500 when the option matures. The article is available here. It is a bit technical, but all you really need to know is how to compute his index (the SVIX, a modified version of the VIX) and he is very explicit about how to do it. It's just a weighted average of certain options, so it's not that complicated.

Moving in this direction, Hilpisch has a book on how to price options using Python. He actually provides snipets of codes and functions to do some of that job. The book is "Derivative Analytics with Python." He also wrote "Python for Finance" and the book "Artificial Intelligence in Finance: A Python-Based Guide." I think you can find the first two online for free if you look long enough, but the 3rd one is really new and I'm pretty you'd have to purchase it or rent it somewhere.

I personally used the second book to learn a few useful things for a course on option pricing in incomplete markets and it is extremely well structured. He is very clear about how you come up with the formulas he uses and, then, he also gives you code that actually runs. If you ever want to move in this direction, the late Peter Christoffersen has a Dropbox link on his personal website which is still running. It's a MATLAB toolbox for pricing European options using GARCH-based models. He only gives you the pricing functions and the papers where he used them, but not the estimation functions. It is still a good start. If you want to put in the effort, you could install a free trial for MATLAB here and you'd have 30 days to translate his code in Python for your personal use.

Short of digging into machine learning, that's about as much as I can suggest.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.