{"word_count": 825, "character_count": 5328}
For the last course MAT8886 of this (long) winter session, on copulas (and extremes), we will discuss risk aggregation. The course will be mainly on the problem of bounding  the distribution (or some risk measure, say the Value-at-Risk) for two random variables with given marginal distribution. For instance, we have two Gaussian risks. What could be be worst-case scenario for the 99% quantile of the sum ? Note that I mention implications in terms of risk management, but of course, those questions are extremely important in terms of statistical inference, see e.g. Fan &#38; Park (2006).This problem, is sometimes related to some question asked by Kolmogorov almost one hundred years ago, as mentioned in Makarov (1981). One year after, Rüschendorf (1982) also suggested a proof of bounds calculation. Here, we focus in dimension 2. As usual, it is the simple case. But as mentioned recently, in Kreinovich &#38; Ferson (2005), in dimension 3 (or higher), "computing the best-possible bounds for arbitrary n is an NP-hard (computationally intractable) problem". So let us focus on the case where we sum (only) two random variable (for those interested in higher dimension, Puccetti &#38; Rüschendorf (2012) provided interesting results for a dual version of those optimal bounds).Let  denote the set of univariate continuous distribution function, left-continuous, on . And  the set of distributions on . Thus,  if  and . Consider now two distributions . In a very general setting, it is possible to consider operators on . Thus, let  denote an operator, increasing in each component, thus that . And consider some function  assumed to be also increasing in each component (and continuous). For such functions  and , define the following (general) operator,  asOne interesting case can be obtained when is a copula, . In that case,and further, it is possible to writeIt is also possible to consider other (general) operators, e.g. based on the sumor on the minimum,where  is the survival copula associated with , i.e. . Note that those operators can be used to define distribution functions, i.e.and similarlyAll that seems too theoretical ? An application can be the case of the sum, i.e. , in that case  is the distribution of sum of two random variables with marginal distributions  and , and copula . Thus,  is simply the convolution of two distributions,The important result (that can be found in Chapter 7, in Schweizer and Sklar (1983)) is that given an operator , then, for any copula , one can find a lower bound for as well as an upper boundThose inequalities come from the fact that for all copula , , where  is a copula. Since this function is not copula in higher dimension, one can easily imagine that get those bounds in higher dimension will be much more complicated...In the case of the sum of two random variables, with marginal distributions  and , bounds for the distribution of the sum , where  and , can be writtenfor the lower bound, andfor the upper bound. And those bounds are sharp, in the sense that, for all , there is a copula  such thatand there is (another) copula  such thatThus, using those results, it is possible to bound cumulative distribution function. But actually, all that can be done also on quantiles (see Frank, Nelsen &#38; Schweizer (1987)). For all  let  denotes its generalized inverse, left continuous, and let  denote the set of those quantile functions. Define then the dual versions of our operators,andThose definitions are really dual versions of the previous ones, in the sense that  and .Note that if we focus on sums of bivariate distributions, the lower bound for the quantile of the sum iswhile the upper bound isA great thing is that it should not be too difficult to compute numerically those quantities. Perhaps a little bit more for cumulative distribution functions, since they are not defined on a bounded support. But still, if the goal is to plot those bounds on , for instance. The code is the following, for the sum of two lognormal distributions .&#62; F=function(x) plnorm(x,0,1)&#62; G=function(x) plnorm(x,0,1)&#62; n=100&#62; X=seq(0,10,by=.05)&#62; Hinf=Hsup=rep(NA,length(X))&#62; for(i in 1:length(X)){+ x=X[i]+ U=seq(0,x,by=1/n); V=x-U+ Hinf[i]=max(pmax(F(U)+G(V)-1,0))+ Hsup[i]=min(pmin(F(U)+G(V),1))}If we plot those bounds, we obtain&#62; plot(X,Hinf,ylim=c(0,1),type="s",col="red")&#62; lines(X,Hsup,type="s",col="red")But somehow, it is even more simple to work with quantiles since they are defined on a finite support. Quantiles are here&#62; Finv=function(u) qlnorm(u,0,1)&#62; Ginv=function(u) qlnorm(u,0,1)The idea will be to consider a discretized version of the unit interval as discussed in Williamson (1989), in a much more general setting. Again the idea is to compute, for instanceThe idea is to consider  and , and the bound for the quantile function at point  is thenThe code to compute those bounds, for a given  is here&#62; n=1000&#62; Qinf=Qsup=rep(NA,n-1)&#62; for(i in 1:(n-1)){+ J=0:i+ Qinf[i]=max(Finv(J/n)+Ginv((i-J)/n))+ J=(i-1):(n-1)+ Qsup[i]=min(Finv((J+1)/n)+Ginv((i-1-J+n)/n))+ }Here we have (several s were considered, so that we can visualize the convergence of that numerical algorithm),Here, we have a simple code to visualize bounds for quantiles for the sum of two risks. But it is possible to go further...