As a part of my PhD program I have to attend the summer school organized by our department. During this summer school Prof. Braun (one of speakers) mentioned a super nice resource of catastrophe bonds (cat bonds) & insurance-linked securities (ILS). It provides the information, such as the size, the trigger etc. about most of ILS.

Apart from the information in the large table, the column “Issuer” contains clicable elemtents, from where one can get an extended information about one or the other ILS. The script conceptually is orginized quite simply: (1) get all links from the table, and (2) go over each link and extranct a contract’s data. As in post about Dortmund real estate we use the rvest package for web-scrapping (read that post if you are not very familiar with web-scrappling).

# load packages
library("rvest")
library("stringr")

base_url <- "http://www.artemis.bm"
url <- "http://www.artemis.bm/deal_directory/"

# get links from Issuer column
html_nodes(".table-style01 a") %>%
html_attr("href"))


The cool thing about these ILS individual “pages” is that they use the same format. I.e. each page orginized as a bullited list, where the beggining of the line is the description of the variable (bold), and after the column the value follows. For instnace, for Spectrum Capital Ltd. special purpose vehicle (4th from the top) the size is \$430m, which comes after word Size:. Thus, we need to keep CSS selectors for each element of the list, as well as the beggining of the phraze, which we will cut in order to get only the value. We also need to define the function which will extract the text from the CSS selector, and cut the start pharze:

selectors <- paste0("#perex li:nth-child(", 1:9, ")")

start_phrazes <- c("Issuer / SPV: ",
"Placement / structuring agent/s: ",
"Risk modelling / calculation agents etc: ",
"Risks / Perils covered: ",
"Size: ",
"Trigger type: ",
"Ratings: ",
"Date of issue: ")

extract <- function(selector, link, start_phraze) {
sub(pattern = start_phraze, replacement =  "", x = x, fixed = TRUE)
}


It’s naturall to keep all the data in data.frame, where rows are ILS, and columns are variables. Finally, the data is downloaded in two nested loops: the first is to go over all securities, and the second one is to extract the each element (CSS selector) of bulited list. Oh, and let’s not forget to spruce up the column names of the data frame :)

ils <- data.frame(matrix(data = NA,
ncol = length(start_phrazes)))

# CAUTION: it's time-consuming, as long as it has to load 479 web-pages