There's multivariate random variable, future prices of assets, 5 years from now: $$X = [Gold, Silver, SP500]$$

There's historical prices for $X$ available for last 50 years. It's possible to fit historical prices to get multivariate probability distribution of future prices


How to fit the multivariate conditional probability distribution? To get better prediction, as (let's suppose it is so) the current prices have predictive power for the future prices.


I don't need the distribution itself, just the ability to sample $X$ given $CurrentX$. If that helps the individual prices have Pareto distribution.

  • $\begingroup$ Bucket by ranges of currentX $\endgroup$ – Ezy Feb 1 at 11:47
  • $\begingroup$ @Ezy but CurrentX is a vector, not scalar, how it could be bucketed? $\endgroup$ – Alex Craft Feb 1 at 11:56
  • $\begingroup$ You can bucket a vector of variables. It’s just more buckets. But you need sufficient amount of data. $\endgroup$ – Ezy Feb 1 at 12:16

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