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looking to implement a realized kernel model to forecast realized variance of around ~140 equities and indices in Python given order book data.

I have read "Realised Kernels in Practice: Trades and Quotes" and the associated papers by Nielsen et al and before reinventing the wheel by implementing from scratch I was hoping someone had a link to a public implementation that I could base my work off of to save time/effort and likely find best practices!

If not, any tips on how to most efficiently implement manually would be much appreciated as there are run time constraints on my model!

Best,

Kareem

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    $\begingroup$ I don't know any Python package that have implemented the Realized kernel method. However, in R there is the highfrequency package developed and maintained by a couple of professors. You can see the source code on Github and try to reimplement the Realized kernel in Python. $\endgroup$
    – Pleb
    Sep 1 at 5:38
  • $\begingroup$ Thanks you! this is great and exactly what I was looking for $\endgroup$ Sep 1 at 5:48

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