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I want to estimate a copula for the innovations of two dependent time series (A and B). I have found no reference with a step by step on how to do this. I have only found summarized papers from which I am not able to get the details.

Would you have a reference for this subject?

[EDIT]

Section 4 of Nonparametric Estimation Of Copulas For Time Series, by JD Fermanian, O Scaillet is an example.

Page 14 of Estimating copula densities through wavelets, by Genest, Masiello, Tribouley is another example. Here the author states that he estimates the copula "once the effect of time has been filtered out". And then he gets figure 9 c). But I do not understand what the author means with "filtering time out".

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  • $\begingroup$ Can you share the summarized papers? $\endgroup$
    – Lipton
    Jan 23, 2018 at 18:09
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    $\begingroup$ @Lipton Sure, I added this information as an EDIT $\endgroup$
    – Pierre
    Jan 23, 2018 at 19:35

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It sounds like you wish to model the joint distribution of some random vector comprised of those 2 time series, so you are interested in time-varying copulas.

There are many ways of estimating copulas, depending on whether they are parametric, nonparametric, etc. Patton Section 2.3 cites a number of papers and textbooks that discuss different methods, so this should help you in your literature search.

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