Try: qqnorm(log(mydat))
That doesn't look too bad, does it? Now: where is the problem? Cheers, B. On Jul 22, 2015, at 12:41 PM, Amelia Marsh <amelia_mars...@yahoo.com> wrote: > Hello! > > (I dont know if I can raise this query here on this forum, but I had already > raised on teh finance forum, but have not received any sugegstion, so now > raising on this list. Sorry for the same. The query is about what to do, if > no statistical distribution is fitting to data). > > I am into risk management and deal with Operatioanl risk. As a part of BASEL > II guidelines, we need to arrive at the capital charge the banks must set > aside to counter any operational risk, if it happens. As a part of Loss > Distribution Approach (LDA), we need to collate past loss events and use > these loss amounts. The usual process as being practised in the industry is > as follows - > > Using these historical loss amounts and using the various statistical tests > like KS test, AD test, PP plot, QQ plot etc, we try to identify best > statistical (continuous) distribution fitting this historical loss data. Then > using these estimated parameters w.r.t. the statistical distribution, we > simulate say 1 miliion loss anounts and then taking appropriate percentile > (say 99.9%), we arrive at the capital charge. > > However, many a times, loss data is such that fitting of distribution to loss > data is not possible. May be loss data is multimodal or has significant > variability, making the fitting of distribution impossible. Can someone guide > me how to deal with such data and what can be done to simulate losses using > this historical loss data in R. > > My data is as follows - > > mydat <- > c(829.53,4000,6000,1000,1063904,102400,22000,4000,4200,2000,10000,400, > 459006, 7276,4000,100,4000,10000,613803.36, > 825,1000,5000,4000,3000,84500,200, 2000,68000,97400,6267.8, > 49500,27000,2100,10489.92,2200,2000,2000,1000,1900, > 6000,5600,100,4000,14300,100,94100,1200,7000,2000,3000,1100,6900,1000,18500,6000,2000,4000,8400,11200,1000,15100,23300,4000,13100,4500,200,2000,50000,3900,3200,2000,2000,67000,2000,500,2000,1000,1900,10400,1900,2000,3200,6500,10000,2900,1000,14300,1000,2700,1500,12000,40000,25000,2800,5000,15000,4000,1000,21000,15000,16000,54000,1500,19200,2000,2000,1000,39000,5000,1100,18000,10000,3500,1000,10000,5000,14000,1800,4000,1000,300,4000,1000,100,1000,4400,2000,2000,12000,200,100,1000,1000,2000,1600,2000,4000,14000,4000,13500,1000,200,200,1000,18000,23000,41400,60000,500,3000,21000,6900,14600,1900,4000,4500,1000,2000,2000,1000,4100,2000,1000,2000,8000,3000,1500,2000,2000,3500,2000,2000,1000,3800,30000,55000,500,1000,1000,2000,62400,2000,3000,200,2! 00! > ! > 0,3500,2000,2000,500,3000,4500,1000,10000,2000,3000,3600,1000,2000,2000,5000,23000,2000,1900,2000,60000,2000,60000,20000,2000,2000,4600,1000,2000,1000,18000,6000,62000,68000,26800,50000,45900,16900,21500,2000,22700,2000,2000,32000,10000,5000,138000,159700,13000,2000,17619,2000,1000,4000,2000,1500,4000,20000,158900,74100,6000,24900,60000,500,1000,40000,10000,50000,800,4000,4900,6500,5000,400,500,3000,32300,24000,300,11500,2000,5000,1000,500,5000,5500,17450,56800,2000,1000,21400,22000,60000,3000,7500,3000,1000,1000,2000,1500,83700,2000,4000,170005,70000,6700,1500,3500,2000,10563.97,1500,25000,2000,2000,2267.57,1100,3100,2000,3500,10000,2000,6000,1500,200,20000,4000,46400,296900,150000,3700,7500,20000,48500,3500,12000,2500,4000,8500,1000,14500,1000,11000,2000,2000,120000,20000,7600,3000,2000,8000,1600,40000,2000,5000,34187.67,279100,9900,31300,814000,43500,5100,49500,4500,6262.38,100,10400,2400,1500,5000,2500,15000,40000,32500,41100,358600,109600,514300,258200,225900,402700,2! 7! > 4300,75000,1000,56000,10000,4100,1000,15000,100,40000,7900,5000,105000 > ,15100,2000,1100,2900,1500,600,500,1300,100,5000,5000,10000,10100,7000,40000,10500,5000,9500,1000,15200,2000,10000,10000,100,7800,3500,189900,58000,345000,151700,11000,6000,7000,15700,6000,3000,5000,10000,2000,1000,36000,1000,500,8000,9000,6000,2000,26500,6000,5000,97200,2000,5100,17000,2500,25500,24000,5400,90000,41500,6200,7500,5000,7000,41000,25000,1500,40000,5000,10000,21500,100,32000,32500,70000,500,66400,21000,5000,5000,12600,3000,6200,38900,10000,1000,60000,41100,1200,31300,2500,58000,4100,58000,42500) > > > Sorry for the inconvenience. I do understand fitting of distribution to such > data is not a full proof method, but this is what is the procedure that has > been followed in the risk management risk industry. Please note that my > question is not pertaining to operational risk. My question is if > distributions are not fitting to a particular data, how do we proceed further > to simualte data based on this data. > > Regards > > Amelia Marsh > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
