Multivariate random number generation for skewed distribution of asset class returns
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 at 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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