Depends on the kind of the strategy you are running- if it is stat arb with thousands of instruments at 20-25% turnover , a simplistic assumption such as a fraction(0.1 to 0.5) of average daily bid ask spread would do just fine . 

 

scottupper

Depends on the kind of the strategy you are running- if it is stat arb with thousands of instruments at 20-25% turnover , a simplistic assumption such as a fraction(0.1 to 0.5) of average daily bid ask spread would do just fine . 

Don't forget market impact.

The more you trade the more you move the price.

Kyle model covers that, but Citigroup came out with more research about 15 years ago.

There might be even more current analysis on this.

Remember, these are both assumptions for simulation as well as levers for controlling the optimizer, and they mean two different things depending on what you are trying to do.

 

Well of course there is market impact - no one can deny it , but unless you are operating at a very large scale(5B+ gross) and/or very high turnover , there is not much value in incorporating it into your models - it would only introduce noise. You can limit your impact simply by targeting no more than x%(1-5%) daily volume on any given day with VWAP execution. The extra effort  to model for impact isn't simply worth it.

 
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scottupper

Well of course there is market impact - no one can deny it , but unless you are operating at a very large scale(5B+ gross) and/or very high turnover , there is not much value in incorporating it into your models - it would only introduce noise. You can limit your impact simply by targeting no more than x%(1-5%) daily volume on any given day with VWAP execution. The extra effort  to model for impact isn't simply worth it.

Point.  We did try to model it off of implementation shortfall.

Guy number one is the person writing the basic alpha signals and tossing it into the vendor optimizer.

Guy number five is the portfolio engineer.

Guy number twenty is basically running the bespoke optimizer when the PM gets too pssd off at dealing with vendors.

Guy number thirty is testing new risk factors and running attribution on them.

Guy number forty is doing all of the internal crossing between different portfolios.  

Guy number sixty is running the bespoke market impact modeling.  And even then, he's sticking his thumb up in the air and coming back with single-digit t-values.

It is not priority #1, but it matters.  And if you can herd all of the cats into a room and lock the door until the PMs can agree to an attribution model that they think is fair, there's a lot of value in a joint book or at least risk crossing and tcost optimization.  With that, you've now got position crossing, risk crossing, and joint tcost optimization.

Figuring out how to do this and keeping all of your PMs and not having it be political or a PNL or power grab is what separates the mediocre managers from the terrific ones.

 

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