[R-SIG-Finance] Multivariate random number generation for skewed distribution of asset class returns

[Jasen Mackie]

You may find the "boot" package in R useful -
https://cran.r-project.org/web/packages/boot/boot.pdf

On Tue, 14 Jan 2020 at 10:57, Eric Berger <ericjberger using gmail.com> wrote:

> 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 <ilya.kipnis using gmail.com> 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 <
> > 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
> > >         [[alternative HTML version deleted]]
> > >
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> >
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