library(fExtremes) library(fEcofin) data(nyse) NYSELevel <- timeSeries(nyse[, 2], charvec = as.character(nyse[, 1])) NYSELoss <- na.omit(-1.0 * diff(log(NYSELevel)) * 100) colnames(NYSELoss) <- "NYSELoss" ## Point process data NYSEPP <- pointProcess(x = NYSELoss, u = quantile(NYSELoss, 0.95)) ## Declustering DC05 <- deCluster(x = NYSEPP, run = 5, doplot = FALSE) DC10 <- deCluster(x = NYSEPP, run = 10, doplot = FALSE) DC20 <- deCluster(x = NYSEPP, run = 20, doplot = FALSE) DC40 <- deCluster(x = NYSEPP, run = 40, doplot = FALSE) DC60 <- deCluster(x = NYSEPP, run = 60, doplot = FALSE) DC120 <- deCluster(x = NYSEPP, run = 120, doplot = FALSE) ## Fit of declustered data DC05Fit <- gpdFit(DC05, u = min(DC05)) DC10Fit <- gpdFit(DC10, u = min(DC10)) DC20Fit <- gpdFit(DC20, u = min(DC20)) DC40Fit <- gpdFit(DC40, u = min(DC40)) DC60Fit <- gpdFit(DC60, u = min(DC60)) DC120Fit <- gpdFit(DC120, u = min(DC40))