# Chapter 1. Financial Markets, Prices and Risk

The original 2011 R code will not fully work on a recent R because there have been some changes to libraries. The latest version of the Matlab code only uses functions from Matlab toolboxes.
##### Listing 1.1: Download S&P-500 data in R Last edited: August 2016

library(tseries)
library(zoo)
price = get.hist.quote(instrument = "^gspc", start = "2000-01-01", quote="AdjClose")
y=diff(log(price))
plot(y)
y=coredata(y)

##### Listing 1.2: Download S&P-500 data in Matlab Last edited: August 2016

price = hist_stock_data('01012000','30082016','^gspc');
y=diff(log(price.Close(end:-1:1)))
plot(y)


##### Listing 1.3: Sample statistics in R Last edited: August 2016

library(moments)
mean(y)
sd(y)
min(y)
max(y)
skewness(y)
kurtosis(y)
acf(y,1)
acf(y^2,1)
jarque.bera.test(y)
Box.test(y, lag = 20, type = c("Ljung-Box"))
Box.test(y^2, lag = 20, type = c("Ljung-Box"))

##### Listing 1.4: Sample statistics in Matlab Last edited: August 2016

mean(y)
std(y)
min(y)
max(y)
skewness(y)
kurtosis(y)
[h,pValue,stat]=lbqtest(y,20)
[h,pValue,stat]=lbqtest(y.^2,20)
[h,pValue,stat]=lbqtest(y,'lags',20)
[h,pValue,stat]=lbqtest(y.^2,'lags',20)


##### Listing 1.5: ACF plots and the Box-Ljung test in R Last edited: August 2016

library(MASS)
library(stats)
q = acf(y,20)
q1 = acf(y^2,20)
plot(q,main="ACF of daily returns")
plot(q1,main="ACF of squared daily returns")

##### Listing 1.6: ACF plots and the Box-Ljung test in Matlab Last edited: August 2016

autocorr(y,'numLags',20)
autocorr(y.^2,'numLags',20)


##### Listing 1.7: QQ plots in R Last edited: August 2016

library(car)
qqPlot(y)
qqPlot(y,distribution="t",df=5)

##### Listing 1.8: QQ plots in Matlab Last edited: 2011

qqplot(y)



price1 = get.hist.quote(instrument = c("msft"),start = "2007-06-01",end = "2009-12-31",quote="AdjClose")
price2 = get.hist.quote(instrument = c("ms"),  start = "2007-06-01",end = "2009-12-31",quote="AdjClose")
price3 = get.hist.quote(instrument = c("GS"),  start = "2007-06-01",end = "2009-12-31",quote="AdjClose")
p=cbind(price1,price2,price3)
y=diff(log(p))
print(cor(y))