Comment coder plein d'exercice selon H

#1
Sharpe<-function(x,y){
(mean(x)-mean(y)/sd(x))
}

#2
covar<-function(x){
M<-t(colMeans(x),dim(x)[2],dim(x)[1])
return(t(x-M)%*%(x-M)/(dim(x)[1]-1))
}

#3
PortRet<-function(w,mu){
w<-as.matrix(w)
mu<-as.matrix(mu)
TT<-(t(w)%*%mu)
return(TT)
}

Portsig<-function(x,cov){
q<-sqrt(t(x)%*%cov%*%x)
return(q)
}

#4
install.packages("tseries")
library("tseries")

getdata<-function(x){
get.hist.quote(x,quote="Close",start="2010-01-01",compression="m")
}


#5
Code.R<-
FCT<-read.csv("FCT.csv",header=TRUE,row.names=1)
Mkt<-FCT$MKT
SP<-Sharpe(Mkt,1)
P<-paste("le ratiod est ",SP,"aaa")
print(P)


####Exercice 2#####

bmw<-getdata("BMW.DE")
telefonica<-getdata("TEF.MC")
oreal<-getdata("OR.PA")
unilever<-getdata("UNA.AS")
unicredit<-getdata("UCG.MI")
eurostoxx<-getdata("^STOXX50E")

FCT<-read.csv("FCT.csv",header=TRUE,row.names=1)

class(FCT)
typeof(FCT)

install.packages("zoo")
library("zoo")
Stocks<-cbind(bmw,telefonica,oreal,unilever,unicredit,eurostoxx)
Stocks<-na.locf(Stocks)

rstocks<-diff(log(Stocks))
install.packages("Quandl")
library("Quandl")
RF<-Quandl("BOF/QS_M_IEUTIO1M", collapse="monthly", trim_start="1999-01-01", trim_end="2015-03-02")

PrimeBMW<-rstocks[,1]-RF
Primetelefonica<-rstocks[,2]-RF
Primeoreal<-rstocks[,3]-RF
Primeunilever<-rstocks[,4]-RF
Primeunicredit<-rstocks[,5]-RF
Primeeurostoxx<-rstocks[,6]-RF

CAPM1<lm(PrimeBMW~Primeeurostoxx)
CAPM2<lm(Primetelefonica~Primeeurostoxx)
CAPM3<lm(Primeoreal~Primeeurostoxx)
CAPM4<lm(Primeunilever~Primeeurostoxx)
CAPM5<lm(Primeunicredit~Primeeurostoxx)

CAPM1$coefficient
CAPM2$coefficient
CAPM3$coefficient
CAPM4$coefficient
CAPM5$coefficient

CAPM1$r.square
CAPM2$r.square
CAPM3$r.square
CAPM4$r.square
CAPM5$r.square

summary(CAPM1)

#4 Fama French
FCT<-zoo(FCT)
MKT<-FCT[,1]
SMB<-FCT[,2]
HML<-FCT[,3]
FFregression<-lm(FCT~MKT+SMB+HML)
summary(FFregression)

#Exo 3
#1
CAC<-as.matrix(getdata("^FCHI"))
DLCAC<-as.matrix(diff(log(CAC)))
install.packages("stats")
install.packages("urca")
library("stats")
library("urca")

#2
#Test de Dikey Fuller:
d<-ur.df(CAC)
dl<-ur.df(DLCAC)
#La s?rie CAC/DLCAC est stationnaire si d/dl appartient ? [-2,0]

#Graph
acf(CAC)
pacf(CAC)

acf(LCAC)
pacf(LCAC)

#3

#Box Jenkins:
AR2<-arima(DLCAC,order=c(2,0,0))
MA1<-arima(DLCAC, order=c(0,0,1))
ARMA21<-arima(DLCAC,order=c(2,0,1))
#Le meilleur mod?le est celui qui a le crit?re d'informatrion le plus faible (AIC)


#####lc.dr<-ur.df(d, type = "trend", lags = 1,
selectlags = c("Fixed", "AIC", "BIC")) #####
#Ex4

#1
vc<-covar(diff(log(STOCKS))
rdt<-na.locf(rend(STOCKS))

#2
install.packages("quadprog")
library("quadprog")

n<-3
Dmat<-vc
dvec<-rdt
Amat<-rbind(rep(1,n),diag(n))
b<-matrix(c(1,rep(0,n)),4,1)
W_opt <- function(Dmat,dvec,Amat,b) { #r:rendements #sig: volatilit?
w<-solve.QP(Dmat,dvec,t(Amat),b,meq=1)
return(w)
}


#3
port_opt<-function(lambda){
solve.QP(lambda*sig,r,t(Amat),b,meq=1)
wo<-t(sapply(lambda,port_opt))
vol_port<-PortSig(Var_cov,wo)


}

rp<-matrix(NA,1,length(lambda))
for(i in 1:length(lambda)){
rp[i]<-PortRet(solution[,i],dvec)
}
rp

vp<-matrix(NA,1,length(lambda))
for(i in 1:length(lambda)){
vp[i]<-portsig(solution[,i],S)
}
vp

plot(vp,rp)