> Ilya suggests to "take chunks of time instead of one observation". In the

> academic literature this method is referred to as the "block bootstrap".

> See, for example, "Bootstraps for Time Series" by Peter Buhlmann, which

> discusses model-based bootstraps, sieve bootstraps and block bootstraps.

> You might also Google these terms to look for other sources of information.

>

> HTH,

> Eric

>

>

> On Tue, Jan 14, 2020 at 5:12 PM Ilya Kipnis <

[hidden email]> wrote:

>

> > 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 <

> >

[hidden email]> 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

> > > [[alternative HTML version deleted]]

> > >

> > > _______________________________________________

> > >

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> > >

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> > > -- Also note that this is not the r-help list where general R questions

> > > should go.

> > >

> >

> > [[alternative HTML version deleted]]

> >

> > _______________________________________________

> >

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> >

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> > -- Also note that this is not the r-help list where general R questions

> > should go.

> >

>

> [[alternative HTML version deleted]]

>

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>

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