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rickyvic


Total Posts: 196
Joined: Jul 2013
 
Posted: 2019-12-19 15:26
Hey guys any pointers at these models for high frequency data?
I am looking at the literature I was thinking of an estimator robust to microstructural noise that works in trade time.
Is this even an important issue or I can do with the normal one?
Found this but I am pretty sure there are better papers to start from
https://arxiv.org/pdf/1811.09312.pdf

"amicus Plato sed magis amica Veritas"

nikol


Total Posts: 909
Joined: Jun 2005
 
Posted: 2020-01-05 20:58
I found this ref because came myself to the following:
Use of (averaging) filter in trade trigger (MATT) design leads to mean-reverting behavior of Price-MATT. Since everyone is using that you should have observed the collapse of the amplitude or constant change of the (averaging) period. Or the change of other parameters of the filter you use. I would call it secondary (market feed-back) effect.

As it is said "they publish only things which do not work".

Sorry for the late response. It is not HF-style, no (hope not a sin).

rickyvic


Total Posts: 196
Joined: Jul 2013
 
Posted: 2020-01-18 21:41
Thanks for the answer I will look into it... My question is how to capture the jump in mean that happens periodically.
I tried a bunch of things in state space but I can't make it fit, while clearly there is a strong negative autocorrelation of returns.
I am also considering adding a jump term instead of a time varying mean, but it is not picking up the jumps correctly. Maybe still need to check I am doing right.

"amicus Plato sed magis amica Veritas"

rickyvic


Total Posts: 196
Joined: Jul 2013
 
Posted: 2020-01-18 21:50
Microstructure noise does not bother a mean reverting portfolio. Jumps do as it is non stationary in level but locally strongly meanreverting although not necessarily stationary.

"amicus Plato sed magis amica Veritas"

nikol


Total Posts: 909
Joined: Jun 2005
 
Posted: 2020-01-18 22:19
This one is interesting to look at.
https://www.risk.net/risk-management/1940305/event-risk-modelling-equities

Basically you measure diffusion part and estimate jump contribution at the same time. When Jump comes (beyond quantile X) take it into Jump, but exclude from diffusion.
As third contribution, you can incorporate your overreaction as mean reverting process on top of diffusion within T-period after jump (parameter). But that might be subtle (I guess).
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