# Optimal Portfolios, or sort of…

Last week, we got our first class on portfolio optimization. We’ve seen Markowitz’s theory where expected returns and the covariance matrix are given,

```> download.file(url="http://freakonometrics.free.fr/portfolio.r",destfile = "portfolio.r") > source("portfolio.r") > library(zoo) > library(FRAPO) > library(IntroCompFinR) > library(rrcov) > data( StockIndex ) > pzoo = zoo ( StockIndex , order.by = rownames ( StockIndex ) ) > rzoo = ( pzoo / lag ( pzoo , k = -1) - 1 ) * 100 > Moments <- function ( x , method = c ( "CovClassic" , "CovMcd" , "CovMest" , "CovMMest" , "CovMve" , "CovOgk" , "CovSde" , "CovSest" ) , ... ) { method <- match.arg ( method ) ans <- do.call ( method , list ( x = x , ... ) ) + return ( getCov ( ans ) )} > covmat=Moments(as.matrix(rzoo),"CovClassic") > (covmat=round(covmat,1)) SP500 N225 FTSE100 CAC40 GDAX HSI SP500   17.8 12.7 13.8 17.8 19.5 18.9 N225    12.7 36.6 10.8 15.0 16.2 16.7 FTSE100 13.8 10.8 17.3 18.8 19.4 19.1 CAC40   17.8 15.0 18.8 30.9 29.9 22.8 GDAX    19.5 16.2 19.4 29.9 38.0 26.1 HSI     18.9 16.7 19.1 22.8 26.1 58.1 > er=apply(as.matrix(rzoo),2,mean) > (er=round(er,1)) SP500 N225 FTSE100 CAC40 GDAX HSI 0.6 -0.2 0.4 0.5 0.8 1.0 > ef <- efficient.frontier(er, covmat, alpha.min=-2.5, alpha.max=2.5, nport=50)```

We can now visualize the efficient frontier (and admissible portfolios) below

```> u=c(12,ef\$sd,12,12) > v=c(5,ef\$er,-1,5) > plot(ef\$sd,ef\$er,type="l",xlab="Standard Deviation",ylab="Expected Return", xlim=c(3.5,11),ylim=c(0,2.5),col="red",lwd=1.5) > points(sqrt(diag(covmat)),er,pch=19,col="blue") > text(sqrt(diag(covmat)),er,names(er),pos=4, col="blue",cex=.6) > polygon(u,v,border=NA,col=rgb(0,0,1,.3))```

That was the starting point of our class. We did also mention that something important was actually hard to visualize on that graph : the correlation between returns. It is not in the points (which are univariate, with expected return and standard deviation), but in the efficient frontier. For instance, here is our correlation matrix

```> (cormat=covmat/(sqrt(diag(covmat) %*% t(diag(covmat))))) SP500 N225 FTSE100 CAC40 GDAX HSI SP500   1.00 0.50 0.79 0.76 0.75 0.59 N225    0.50 1.00 0.43 0.45 0.43 0.36 FTSE100 0.79 0.43 1.00 0.81 0.76 0.60 CAC40   0.76 0.45 0.81 1.00 0.87 0.54 GDAX    0.75 0.43 0.76 0.87 1.00 0.56 HSI     0.59 0.36 0.60 0.54 0.56 1.00```

We can actually change the correlation between FT500 and FTSE100 (which is here .786)

```courbe=function(r=.786){ R=cormat R[1,3]=R[3,1]=r covmat2=(sqrt(diag(covmat) %*% t(diag(covmat))))*R ef <- efficient.frontier(er, covmat2, alpha.min=-2.5, alpha.max=2.5, nport=50) plot(ef\$sd,ef\$er,type="l",xlab="Standard Deviation",ylab="Expected Return", xlim=c(3.5,11),ylim=c(0,2.5),col="red",lwd=1.5) points(sqrt(diag(covmat)),er,pch=19,col=c("blue","red")[c(2,1,2,1,1,1)]) text(sqrt(diag(covmat)),er,names(er),pos=4,col=c("blue","red")[c(2,1,2,1,1,1)],cex=.6) polygon(u,v,border=NA,col=rgb(0,0,1,.3)) }```

for instance, with a correlation of 0.6, we get the following efficient frontier

`> courbe(.6)`

and with a stronger correlation

`> courbe(.9)`

So clearly, correlation does matter. A lot. But more important, one should keep in mind that expected returns and covariances are not given, but estimated. Previously, we did use the standard estimator for the variance matrix. But another (more robust) estimator can be considered

```covmat=Moments(as.matrix(rzoo),"CovSde") er=apply(as.matrix(rzoo),2,mean) ef <- efficient.frontier(er, covmat, alpha.min=-2.5, alpha.max=2.5, nport=50) plot(ef\$sd,ef\$er,type="l",xlab="Standard Deviation",ylab="Expected Return",xlim=c(3.5,11),ylim=c(0,2.5),col="red",lwd=1.5) points(sqrt(diag(covmat)),er,pch=19,col="blue") text(sqrt(diag(covmat)),er,names(er),pos=4,col="blue",cex=.6) polygon(u,v,border=NA,col=rgb(0,0,1,.3))```

It did influence (horizontal) position of points, since variances are now different, as well as the efficient frontier, with clearly much lower variances that can be reached.

And to illustrate a last point, to illustrate the fact that we do have estimators based on observed returns, what if we had observed different ones? A way to get an idea of what might happened is to use bootstrap, e.g. of daily returns.

```> covmat=Moments(as.matrix(rzoo),"CovClassic") > er=apply(as.matrix(rzoo),2,mean) > ef <- efficient.frontier(er, covmat, alpha.min=-2.5, alpha.max=2.5, nport=50) > a=sqrt(diag(covmat)) > b=er > k=1 > plot(ef\$sd,ef\$er,type="l",xlab="Standard Deviation",ylab="Expected Return", xlim=c(3.5,11),ylim=c(0,2.5),col="white",lwd=1.5) > polygon(u,v,border=NA,col=rgb(0,0,1,.3)) > for(i in 1:100){ + id=sample(nrow(rzoo),replace=TRUE) + covmat=Moments(as.matrix(rzoo)[id,],"CovClassic") + er=apply(as.matrix(rzoo)[id,],2,mean) + points(sqrt(diag(covmat))[k],er[k],cex=.5) + }```

or for another asset

Here is what we got on the (estimated) efficient frontier

```> covmat=Moments(as.matrix(rzoo),"CovClassic") > er=apply(as.matrix(rzoo),2,mean) > ef <- efficient.frontier(er, covmat, alpha.min=-2.5, alpha.max=2.5, nport=50) > plot(ef\$sd,ef\$er,type="l",xlab="Standard Deviation",ylab="Expected Return", xlim=c(3.5,11),ylim=c(0,2.5),col="white",lwd=1.5) > points(sqrt(diag(covmat)),er,pch=19,col="blue") > text(sqrt(diag(covmat)),er,names(er),pos=4, col="blue",cex=.6) > polygon(u,v,border=NA,col=rgb(0,0,1,.3)) > for(i in 1:100){ + id=sample(nrow(rzoo),replace=TRUE) + covmat=Moments(as.matrix(rzoo)[id,],"CovClassic") + er=apply(as.matrix(rzoo)[id,],2,mean) + ef <- efficient.frontier(er, covmat, alpha.min=-2.5, alpha.max=2.5, nport=50) + lines(ef\$sd,ef\$er,col="red") + }```

Thus, it is somehow rather difficult to assess wheter a portfolio is optimal, or not… At least from a statistical perspective….

# Actuariat de l’Assurance Non-Vie #5

Petit retour sur les modèles de comptage, avec la digression sur les GLM, avec les modèles permettant d’avoir de la surdispersion (théoriquement impossible dans un modèle de Poisson, mais plus réaliste compte tenu des particularités des données d’assurance). Les slides sont en ligne.