For anybody interested, I will be presenting these things Wednesday evening on a complimentary webinar hosted by Financial Risk Hub.
Registration here: www.financialriskhub.com/deep-learning-applications-in-valuation-and-risk-of-trading-books/
Slides here: www.deep-analytics.org
The deepest bias-variance analysis I have come across is in Christopher Bishop's book, available online for free. But this is heavy material, not for the faint of heart, and this is all pre deep learning, although it remains eminently current.
Hi Daniel, I recommend Andrew Ng's lessons on Coursera for a start, he is an incredibly good teacher (makes me jealous :) Start with the Machine Learning course, and carry on with the Deep Learning specialization.
For the more theoretically inclined, Andrew Ng gives the same courses at...
I just published with my colleague Brian Huge the result of 6m+ research at Danske Bank on pricing and risk approximation by AI. We found that the combination of ML with automatic differentiation (AAD) makes a rather spectacular difference.
The working paper is available on arXiv, pending...
Thank you. The small paragraph explaining AAD is very good indeed.
How do I reach out to these guys please? And could you maybe introduce us by email so that I don’t once again knock unannounced...
Indeed, there is a lot of confusion around AD, AAD, automatic/algorithmic/adjoint forward/reverse diff, backprop, and so forth. and all the names don't help. You may find Leif Andersen's preface to my book (Modern Computational Finance: AAD and Parallel Simulations (Table of Contents and...
Thank you Daniel, I will have a look at your first link.
Boost autodiff is not ad. It does not implement adjoint differentiation (also called reverse-mode ad) with the magic constant time speed, but the so-called forward-mode ad (here, a is for automatic, not adjoint), which is trivial to...
I presented AAD on Bloomberg Tech Talks in November, where I was asked to explain adjoint differentiation, backpropagation and how it all works in finance in just 15 min. My talk was recorded and posted on youtube, hopefully it helps people getting started with these ground breaking technologies.
For avoidance of doubt I forwarded your question to Brian Huge. His answer is as I expected:
If gamma = 1 then the vol of vol function is the same as in SABR, so in case you use the same approximation or pricing method it will be the same.
Hope this helps.
SmoothStep is monotonous and positive but its convexity changes midway between knots.
SmoothStep is well suited to risk views because it is local and interpolates a bell-shaped bump as Jherek mentioned. But I would not recommend it for the construction of the underlying yield curve, especially...
Hi Jherek.
No worries, I will buy the Kindle version of your book as soon as it is available. I teach volatility at Copenhagen University and I am looking forward to reading it. I looked at the TOC and could see that many important themes for modern equity derivatives risk management are...
Oh thank you for flagging this. It is fixed now. BTW looking at your book, I noticed that you did not release an ebook due to the difficulty of rendering equations correctly. I myself had the same problem: Amazon released a Kindle version with unreadable equations, but then, Wiley found a way to...
The AAD framework is generic in the sense that it is reusable for many problems without modification of the AAD code. It is also non-invasive in the sense that the calculations in the instrumented code are unchanged.
But it is invasive in the sense that the calculation code must be templated for...
Of course! Thank you Daniel.
This is part of the toy code, where I don’t worry about performance. In the ‘professional’ code I should never pass collections by value (unless I may still have mistakes left...)
This being said, the inputs are marked const so the compiler should catch and...
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