# R - Programs for Financial Time Series

Feb 3rd, 2012
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Manchester College
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This document outlines how to fit simple ARCH model, test for normality of data, compute descriptive statistics on the data. The student should try these function using a different set of data to enhance learning.

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library(fGarch)library(fBasics)setwd("C:/Documents and Settings/Administrator/Desktop/4th Year")simple_ret=read.table("d-aapl3idx-0110.txt",header=T);simple_ret#Question1#(a)Transforming to percentage returns and obtaining the descriptive statisticsprice_value.ret1=100*(simple_ret\$aapl);price_value.ret1price_value.ret2=100*(simple_ret\$vw);price_value.ret2price_value.ret3=100*(simple_ret\$ew);price_value.ret3price_value.ret4=100*(simple_ret\$sp);price_value.ret4basicstat=cbind(basicStats(price_value.ret1),basicStats(price_value.ret2),basicStats(price_value.ret3),basicStats(price_value.ret4)); basicstat#(b)Transforming Apple stock returns to percentage log returnsprice_value.logret=100*log(1+simple_ret\$aapl);price_value.logretbasicStats(price_value.logret)#(c)jarque.bera.test for normalitylibrary(tseries)jarque.bera.test(price_value.logret)#Question 2#(1)Transforming to percentage log returns and obtaining the descriptive statisticssimple_ret1=read.table ("m-xomsp-8010.txt",header=T);simple_ret1price_value.logret1=100*log(1+simple_ret1\$xom);price_value.logret1price_value.logret2=100*log(1+simple_ret1\$sp);price_value.logret2basicstat1=cbind(basicStats(price_value.logret1),basicStats(price_value.logret2));basicsta1#(2)Normality testnormalTest(price_value.logret1, method = c("sw", "jb"), na.rm = FALSE)normalTest(price_value.logret2, method = c("sw", "jb"), na.rm = FALSE)#(3)Computing Correlation Coefficient, Kendall tau , and Spearman's rho

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