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rickyvic


Total Posts: 204
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: 993
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: 204
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: 204
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: 993
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).

rickyvic


Total Posts: 204
Joined: Jul 2013
 
Posted: 2020-01-21 17:54
I have the same approach also with added scheduled events and so on...
I have fitted a model that attempts to predict the jump component, so first estimate the jump size and probability then try to predict it.
Something is not working on that side as I can predict it but it happens too often. Using B-N jump test and a regression model to predict it.

The jump reversion type is an option too assuming you can measure correctly the jump.

Since it is for a hedged portfolio I am looking for a dynamic beta that adds a degree of uncertainty on whether to hedge or not. That solves the issue of non stationarity a bit as the portfolio is always bounded and mean reverting (with the problem of a time varying beta).

So the way I see is:
1) make a time varying mean capturing the jump
2) detect and model a jump or no jump regime
3) make the portfolio stationary at the cost of re-hedging
4) all of them combined

"amicus Plato sed magis amica Veritas"

nikol


Total Posts: 993
Joined: Jun 2005
 
Posted: 2020-01-27 17:27
Sorry, lost your response.

Jump = intensity (probability) + shape of amplitude.
Both parameters are function of cut-off (separator) from diffusion and perhaps of some other parameters. Or you can simulate diffusion+jump as a joint distribution as well.

> I can predict it but it happens too often.

Check sensitivity to cut-off parameter - do you cut by calibrated sigma of (Brownian) diffusion or by quantile of (non-Brownian) distribution or you blend (convolute) them..

GARCH or mix of two diffusion processes might give you same fat tails.

Found this :
"Detecting Jumps in High-Frequency Prices Under Stochastic Volatility: A Review and a Data-Driven Approach", by Ping-Chen Tsai and Mark B. Shackleton. (book: "HANDBOOK OF HIGH-FREQUENCY TRADING AND MODELING IN FINANCE").

rickyvic


Total Posts: 204
Joined: Jul 2013
 
Posted: 2020-02-18 13:57
Thank you I will be looking into it.

"amicus Plato sed magis amica Veritas"
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