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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

$$P(X)$$

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.

$$P(X|CurrentX)$$

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

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  • $\begingroup$ 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? $\endgroup$ – Alexey Petrushin 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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