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I would like to to estimate the future returns of some public indeces. I have several of them so it is a multivariate problem. The series are quarterly and the estimation should be of at least 15-20 samples ahead.

I have tried so far with a VAR-VARMA model, but although i see great performance in-sample it is very bad out-of samples, showing it is prone to overfitting.

Now I know this is a 1 million dollar question, but in reality I would just like to have an advice on other methods I could try.

I was thinking at copula + MC resampling, or copula + markov chain MC resampling, or maybe adopting recurrent neural networks or others.

Can anybody suggest me on this?

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  • $\begingroup$ What is the motivation for doing multivariate return forecasting? Portfolio- and/or risk-management purposes? Is it important to find some cross-sectional relationship between the Indices (ie. modeling risk-spillovers or constructing a "diversified" portfolio) or do you just want the "best" predictive model that on average provide the best forecasts across indices (then multivariate modelling can be disregarded). $\endgroup$
    – Pleb
    Dec 13, 2021 at 13:43
  • $\begingroup$ the reason behind this is that after I have done this forecast, this has to feed another model. The variables I am talking about here are in reality input variables for a supervised ML model. In order to predict the Y variable of such model I need to predict these input variables $\endgroup$
    – Dark2018
    Dec 13, 2021 at 16:06

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