[R-SIG-Finance] Multivariate random number generation for skewed distribution of asset class returns
Ilya Kipnis
||y@@k|pn|@ @end|ng |rom gm@||@com
Tue Jan 14 16:12:03 CET 2020
This is a question I was actually asked by the head of AI/ML for a fairly
large company and I'll give the same answer here:
Perform the bootstrapping of your choice. That is, take the empirical
returns, and just sample from them. If you want to preserve
autocorrelations, take chunks of time instead of one observation. If you
want to add some random noise, feel free to create some noise distributions
as well.
Hope this helps.
On Tue, Jan 14, 2020 at 9:32 AM shawn tan via R-SIG-Finance <
r-sig-finance using r-project.org> wrote:
> Hi R-SIG-Finance mailing list,
> I have a query about performing a Monte Carlo random number generation for
> asset class returns which accounts for the distribution of the asset class
> (mean, variance, skewness and possibly kurtosis) while also taking into
> consideration the correlation/covariance matrix of the asset classes.
> I came across the R package, mvtnorm, which is able to take the asset
> classes' means, covariance matrix for a normal distribution, through the
> function rmvnorm(n, mean = muvec, sigma = covmat), where n is number of
> trials, mean is the mean vector and sigma is the covariance matrix.
> However, this package does not allow for a skewed distribution or excess
> kurtosis. Historical data for my asset class returns show both positive and
> negative skewness. Additionally, the Johnson distribution function in R
> package, SuppDists, does not seem to account for covariances as inputs.
> Hence, is there an R package/function that allows me to perform the random
> number generation for multivariate returns, which accounts for mean,
> variance, correlation, skewness and even kurtosis as inputs under the Monte
> Carlo simulation?
> Thank you
> Best regards,
> Sjedi
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>
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