I am reading paper "Tactical Investment Algorithms" (link) (NOTE: you can download the paper without registration, just press "Download" and then "Download without registration") by the famous Marcos López de Prado. On the page 8 he writes

In practice, it takes only a few recent observations for the estimated distribution of probability to narrow down the likely DGPs. The reason is, we are comparing two samples, where the synthetic one is comprised of potentially millions of datapoints, and it typically does not take many observations to discard what DGPs are inconsistent with recent observations.
Another possibility is to create a basket of securities with a returns distribution that matches the distribution of a given DGP. Under this alternative implementation, rather than estimating the probability that a security follows a DGP (Data Generating Process), we create a synthetic security (as a basket of securities) for which a given algorithm is optimal.

What does "estimating the probability that a security follows a DGP" mean? What is the probability that the sample is from the distribution?

  • $\begingroup$ The link requires login. Could you edit the quote to include the sentence(s) that precedes what you currently show so that we can see the "first possibility" that comes before the shown "another possibility"? $\endgroup$ – develarist Oct 7 '20 at 16:02
  • $\begingroup$ Dear deveralist, you do not need to register. Just press Download and then scroll down and press "Download without registration". $\endgroup$ – ABK Oct 8 '20 at 11:57
  • $\begingroup$ I dont think anyone will $\endgroup$ – develarist Oct 8 '20 at 12:08
  • $\begingroup$ I included the previous paragraph. $\endgroup$ – ABK Oct 8 '20 at 15:32
  • $\begingroup$ Why don’t you ask the famous Marcos López de Prado directly? There is corresponding author contact info on every paper published. Make sure to tell him how famous he is. $\endgroup$ – user50421 Oct 16 '20 at 7:52

Think of it this way. This has to do with using empirical data or artificial data under the pretense that whichever approach you choose must comply with a pre-specified DGP, installed merely to make the model parametric, for example. There are two possibilities:

  1. Accept the real empirical data for what it is and estimate the probability that the empirical security's "properties" matches the parametric DGP in order to have an idea of how much estimation error might be incurred so that results can be adjusted to nominal levels afterwards, or
  2. Simulate artificial data, that is already designed to match the given parametric DGP, so that you don't need to match the articial security to the parametric DGP since the artificial security already matches it by design

He's not saying anything ground-breaking. Researchers are just as much interested in testing their financial models on both artificial data and empirical data to prove the model's robustness. If it's too much work to match (not well-defined small-sample) empirical data to a well-defined continuous parametric DGP, then it is merely more convenient and less of a head-ache to get on with the experiments demonstrating the model's efficacy without trying to force-feed non-compliant empirical data into the model, which would be the path to follow to at least confirm the theoretical model.

  • $\begingroup$ Dear @develarist, thank you for the reply. Could you, please, explain what do you mean by "estimate the probability that the empirical security's "properties" matches the parametric DGP". Could you, please, give an example of computation this "probability"? $\endgroup$ – ABK Oct 9 '20 at 13:42
  • $\begingroup$ Dear @develarist, in my opinion, your current answer is almost writing the question in other words. $\endgroup$ – ABK Oct 18 '20 at 14:07

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