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library(fGarch)
library(timeSeries)
import= read.csv("Book.csv");import
D2=import$STOCK;D2
plot(D2)
ts.plot(D2)
log.D2=diff(D2);log.D2
plot(log.D2)
ts.plot(log.D2)
acf(log.D2)
pacf(log.D2)
s=log.D2^2;s
plot(s)
ts.plot(s)
M2=garchFit(~garch(1,1),data = log.D2,trace = F);M2
summary(M2)
plot(M2, which=1)
plot(M2, which=6)
plot(M2,which=13)

#EGARCH
library(rugarch)
library(forecast)
C1= read.csv("Book.csv");C1
C2=C1$STOCK;C2
C3=diff(D2);C3
M3=ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(1,1)),mean.model=list(armaOrder=c(0,0),include.mean=F),distribution.model="norm");M3
M3fit=ugarchfit(C3,spec=M3);M3fit
plot(M3fit, which=10)
plot(M3fit, which=8)
plot(M3fit, which=1)
summary(M3fit, which=10)
summary(M3fit,which=8)
summary(M3fit,which=1)
#plotting the forecast
forc=ugarchforecast(fitORspec=M3fit,n.ahead = 12);forc
plot(forc, which=1)
plot(forc, which=3)


#QUESTION TWO
#Kaplan Meier models
library(survival)
library(survminer)
d1=data.frame(time=c(2,4,6,10),event=c(1,0,1,1))
kmc=with(d1,Surv(time,event));
kmc
plot(kmc)
plot(kmc,fun="cumhaz")
kmc2=survfit(Surv(time,event)~1,data=d1);kmc2
summary(kmc2)

#QUESTION TWO
#Importing Libraries
library(survival)
library(survminer)

#Dataframe
D1=data.frame(time=c(1,2,2,3,4,5,6,8,9,10),event=c(0,1,0,1,0,1,0,1,0,1))
kmc=with(D1,Surv(time,event));kmc

#Plotting Kaplan-Meir curve grid() plot(kmc, col = “blue”,
#Plotting Kaplan-Meir curve grid() plot(kmc, col = “blue”,
#Fitting the Kaplan-Meir Model
kmc2 = surv_fit(Surv(time, event) ~ 1, data = D1); kmc2

summary(kmc2)

#Plotting Kaplan-Meir curve
grid()
plot(kmc, 
     col = "blue",           
     lwd = 2,                  
     xlab = "Time",            
     ylab = "Survival Probability",  
     main = "Kaplan-Meier Curve") 

# Plotting cumulative hazard 
grid()
plot(
  kmc2,
  fun = "cumhaz",      
  col = "purple",
  lwd = 2,
  xlab = "Time (Days)",
  ylab = "Cumulative Hazard",
  main = "Cumulative Hazard Function"
)
grid()