mages

## The following example demonstrates
## how a serach and replace string task 
## can be peformed with R across several files

## Create two text files with content 
filenames <- c( tempfile(), tempfile() )
for( f in  filenames ){
 cat("We wish you a Merry Christmas!\n\nBest regards\n", file=f)
}

mages

# Based on code by Paul McWhorter:
# http://www.toptechboy.com/tutorial/python-with-arduino-lesson-11-plotting-and-graphing-live-data-from-arduino-with-matplotlib/

import serial 
import re 
import csv 
import numpy as np 
import matplotlib.pyplot as plt 
from drawnow import *

mages

library(mnormt)
mycols <- topo.colors(100,0.5)
xhat <- c(0.2, -0.2)
Sigma <- matrix(c(0.4, 0.3, 
                  0.3, 0.45), ncol=2)
x1 <- seq(-2, 4,length=151)
x2 <- seq(-4, 2,length=151)
f <- function(x1,x2, mean=xhat, varcov=Sigma) 
  dmnorm(cbind(x1,x2), mean,varcov)
z <- outer(x1,x2, f)

mages

Insurance Data Science Conference 2018

Abstracts

Modelling longevity risk for retirement and pension planning

Gareth Peters, Heriot-Watt University, Edinburgh

In this talk we build on a sequence of papers recently developed to enhance the modelling of life expectancy based on mortality data. Forecasting life expectancy and mortality are two important aspects for the study of demography that influence pension planning, retirement decisions and government policy.

We demonstrate how to develop regression models incorporating stochastic factors such as graduation temporal effects, period effects, cohort effects, stochastic volatility and long memory to enhance the forecasting and estimation of life tables. In addition, we show the mispricing that occurs in standard annuities, pure endowments and Gauranteed Annuity Oprtions (GAOs)

mages

### R code from Computational Actuarial Science with R, Chapter 15
### Author: Markus Gesmann

options(prompt = "R> ", digits = 4, show.signif.stars = TRUE)
options(continue="   ")
library(ChainLadder)
library(lattice)
library(AER)
library(fitdistrplus)
lattice.options(default.theme = standard.theme(color = FALSE))

mages

library(rstan)
stanmodelcode <- "
data {
  int<lower=0> N;
  int<lower=0> y[N];
}
parameters {
  real<lower=0.00001> Theta;
}
model {

mages

snip <- function(input) {
    pb <- pipe("pbcopy", "w")
    write.table(input, sep="\t", file=pb, row.names = FALSE)
    close(pb)
}

mages

# Markus Gesmann
library(arm) # for 'display' function only
icecream <- data.frame(
  # http://www.statcrunch.com/5.0/viewreport.php?reportid=34965&groupid=1848
  temp=c(11.9, 14.2, 15.2, 16.4, 17.2, 18.1, 
         18.5, 19.4, 22.1, 22.6, 23.4, 25.1),
  units=c(185L, 215L, 332L, 325L, 408L, 421L, 
          406L, 412L, 522L, 445L, 544L, 614L)
)
basicPlot <- function(...){

mages

R in Insurance

Cass Business School, London, 14 July 2014

9:00 - 10:00 Opening keynote:
Montserrat Guillen and Leo Guelman: New trends in predictive modelling - the uplift models success story

10:00 - 11:00 Session 1: Reserving (20 min. each)

1. Munir Hiabu: RBNS preserving double chain ladder
2. Els Godecharle: [Reserving by conditioning on markers of individual

mages

## Add an alpha value to a colour
add.alpha <- function(col, alpha=1){
  if(missing(col))
    stop("Please provide a vector of colours.")
  apply(sapply(col, col2rgb)/255, 2, 
                     function(x) 
                       rgb(x[1], x[2], x[3], alpha=alpha))  
}