TALLER DE TEORIA DE RIESGO

library(quantmod)

## Warning: package 'quantmod' was built under R version 3.5.2

## Loading required package: xts

## Warning: package 'xts' was built under R version 3.5.2

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 3.5.2

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: TTR

## Warning: package 'TTR' was built under R version 3.5.2

## Version 0.4-0 included new data defaults. See ?getSymbols.

library(graphics)
library(readxl)
library(moments)

## Warning: package 'moments' was built under R version 3.5.2

library(stats)
library(fTrading)

## Warning: package 'fTrading' was built under R version 3.5.2

## Loading required package: timeDate

## 
## Attaching package: 'timeDate'

## The following objects are masked from 'package:moments':
## 
##     kurtosis, skewness

## Loading required package: timeSeries

## Warning: package 'timeSeries' was built under R version 3.5.2

## 
## Attaching package: 'timeSeries'

## The following object is masked from 'package:zoo':
## 
##     time<-

## Loading required package: fBasics

## Warning: package 'fBasics' was built under R version 3.5.2

## 
## Attaching package: 'fBasics'

## The following object is masked from 'package:TTR':
## 
##     volatility

## 
## Attaching package: 'fTrading'

## The following object is masked from 'package:TTR':
## 
##     SMA

library(readxl)
TALLER_CELSIA <- read_excel("C:/Users/windows/Desktop/PREGRADO 2019-1/TEORIA DE RIESGO/TALLER CELSIA.xlsx")
View(TALLER_CELSIA)
head(TALLER_CELSIA)

1 CALCULO DE RENDMIENTOS

R<-(TALLER_CELSIA$Rendimientos)
head(R)

## [1]  0.000000000 -0.006222795  0.003738322  0.001243008  0.008658063
## [6]  0.008583744

DESCRIPTIVOS

summary(R)

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0544513 -0.0042736  0.0000000  0.0001259  0.0048822  0.0454359

En la media Se puede observar en los datos que la media de los rendimientos esta en 0.0001259 un valos positivo apesar de que llego a un valor minimoen perdidas

Media

mean(R)

## [1] 0.00012586

En la varianza podemos apreciar que la variabilidad de los rendimientos con respecto a su media es de 0.0001067

Varianza

var(R)

## [1] 0.0001067852

La desviacion estandar nos indica la dispercion que tienen los rendimientos con respecto a los rendimientos

Desviacion

sd(R)

## [1] 0.01033369

Asimetria

skewness(R)

## [1] -0.5276258
## attr(,"method")
## [1] "moment"

curtosis

kurtosis(R)

## [1] 4.387975
## attr(,"method")
## [1] "excess"

3.TALLOS Y HOJAS

stem(R)

## 
##   The decimal point is 2 digit(s) to the left of the |
## 
##   -5 | 43
##   -4 | 
##   -4 | 4
##   -3 | 6
##   -3 | 10
##   -2 | 7555
##   -2 | 32111000
##   -1 | 998877666665555
##   -1 | 4444443333222221111000000000000
##   -0 | 999999999998888877777777777766666666666665555555555555
##   -0 | 44444444444444443333333333333333333322222222222222222222222222222222+23
##    0 | 00000000000000000000000000000000000000000000000000000000001111111111+77
##    0 | 5555555555556666666666666777777888888888888889999999999
##    1 | 0000000000000111111222222223333333344
##    1 | 555555566677888888999
##    2 | 00011344
##    2 | 556
##    3 | 12
##    3 | 5
##    4 | 
##    4 | 5

4.CAJAS

boxplot(R, main = "RENDIMIENTOS CELSIA")

5. HISTOGRAMA,AGREGANDO CURVA NORMAL

hist(R,breaks = 20, prob = TRUE, main = "RENDIMIENTOS CELSIA")
lines(density(R))

6. GRAFICO DE LINEAS

a <- zoo(TALLER_CELSIA$Rendimientos, TALLER_CELSIA$Fecha)
b <- as.xts(a,as.Date(TALLER_CELSIA$Fecha))
D <- as.quantmod.OHLC(b, c("Rendimientos"))
chartSeries(D, type = "line", theme = "white", pch = 16, line.type="l")
qqline(R)

7. GRAFICO DE PROBABILIDAD NORMAL

qqnorm(R)

qqnormPlot(R)

PRUEBA DE NORMALIDAD

shapiro.test(R)

## 
##  Shapiro-Wilk normality test
## 
## data:  R
## W = 0.93639, p-value = 7.16e-14

varianza

ren = R^2
var2<- sum(ren)/length(R)
var2

## [1] 0.00010659

VENTANAS MOVILES

library(caTools)

## Warning: package 'caTools' was built under R version 3.5.2

n = 506
k = 25
vem<- runsd(R , k,center = runmean(R,k))
v.venta<-vem[n]^2
plot(vem)

SUAVIZAMIENTO EXPONENCIAL

L<-1:n
lambda<-0.94
lambda2<-lambda^(L-1)
G<-R^2
S<- lambda2*G
t<-sum(S)
sigmat<- (1-lambda)*t
suavexpo<-cbind(L,R,lambda2,G,sigmat)
head(suavexpo)

##      L            R  lambda2            G       sigmat
## [1,] 1  0.000000000 1.000000 0.000000e+00 6.816887e-05
## [2,] 2 -0.006222795 0.940000 3.872318e-05 6.816887e-05
## [3,] 3  0.003738322 0.883600 1.397505e-05 6.816887e-05
## [4,] 4  0.001243008 0.830584 1.545069e-06 6.816887e-05
## [5,] 5  0.008658063 0.780749 7.496205e-05 6.816887e-05
## [6,] 6  0.008583744 0.733904 7.368066e-05 6.816887e-05

sigma<-sqrt(sigmat)
sigma

## [1] 0.008256444

TALLER DE TEORIA DE RIESGO

ELIANA PAOLA MALAMBO