primer paso importamos los datos

library(readr)
data <- read_csv("C:/Users/51960/Downloads/Index2018.csv")
## Parsed with column specification:
## cols(
##   date = col_character(),
##   spx = col_double(),
##   dax = col_double(),
##   ftse = col_double(),
##   nikkei = col_double()
## )
head(data)
## # A tibble: 6 x 5
##   date         spx   dax  ftse nikkei
##   <chr>      <dbl> <dbl> <dbl>  <dbl>
## 1 07/01/1994  470. 2225. 3446. 18124.
## 2 10/01/1994  475. 2225  3441. 18443.
## 3 11/01/1994  474. 2228. 3414. 18485.
## 4 12/01/1994  474. 2182. 3372. 18794.
## 5 13/01/1994  472. 2142. 3360. 18577.
## 6 14/01/1994  475. 2151. 3401. 18974.

arreglamos el formato de las fechas

 #solo especifico que lo queiro como Año-Mes-Dia
data$date<-format(as.Date(data$date,format = "%d/%m/%Y"),"%Y-%m-%d")
data$date<-as.Date(data$date,format = "%Y-%m-%d")

seleccionamos los datos trabajados en la clase que son los precios de ftse

data_new <- data.frame(Fechas=data$date,FTSE=data$ftse)
head(data_new)
##       Fechas    FTSE
## 1 1994-01-07 3445.98
## 2 1994-01-10 3440.58
## 3 1994-01-11 3413.77
## 4 1994-01-12 3372.02
## 5 1994-01-13 3360.01
## 6 1994-01-14 3400.56

*vemos el ACF y PCF del los precios

attach(data_new)
acf(FTSE)

pacf(FTSE)

pacf(FTSE)

## calculamos los retornos con la libreria “timeSeries”

library(timeSeries)
## Loading required package: timeDate
retornos<- returns(data_new$FTSE,method="discrete")
head(retornos)
##              [,1]
## [1,]           NA
## [2,] -0.001567043
## [3,] -0.007792291
## [4,] -0.012229881
## [5,] -0.003561663
## [6,]  0.012068416

agrupamos todo en un solo data frame

Retornos= data.frame(Fechas=data_new$Fechas,Retornos=retornos)
Retornos=Retornos[-1,]
head(Retornos)
##       Fechas     Retornos
## 2 1994-01-10 -0.001567043
## 3 1994-01-11 -0.007792291
## 4 1994-01-12 -0.012229881
## 5 1994-01-13 -0.003561663
## 6 1994-01-14  0.012068416
## 7 1994-01-17  0.002137883

hacemos un grafico de los retornos

library(ggplot2)
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(ggthemes)
ata= ggplot(Retornos,aes(Fechas,Retornos))+geom_line(color="#BF00FF")+labs(title = "RETORNOS",subtitle = "FTSE",caption = "Hecho por Sebastián Sosa Pérez")+
  theme_ft_rc()
ata
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
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con el paquete ploty

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:timeSeries':
## 
##     filter
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(ata)

ACF y PACF

library(forecast)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
tsdisplay(Retornos$Retornos)

ACF y PACF con ploty

ACF

daffy=autoplot(Acf(Retornos$Retornos,lag.max = 40),col="#FF00FF",size=1)+labs(title = "ACF de retornos",caption = "Hecho por Sebastián Sosa Pérez")+theme_economist()

daffy

ggplotly(daffy)

### PACF

daffy=autoplot(Pacf(Retornos$Retornos,lag.max = 40),col="#2ECCFA",size=1)+labs(title = "PACF de retornos",caption = "Hecho por Sebastián Sosa Pérez")+theme_ft_rc()

daffy
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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ggplotly(daffy)

creacion de los respectivos modelos AR()

library(tseries)
AR_1= arma(Retornos$Retornos,order = c(1,0))
summary(AR_1)
## 
## Call:
## arma(x = Retornos$Retornos, order = c(1, 0))
## 
## Model:
## ARMA(1,0)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -8.889e-02 -5.310e-03 -3.024e-05  5.541e-03  9.778e-02 
## 
## Coefficient(s):
##             Estimate  Std. Error  t value Pr(>|t|)
## ar1       -0.0171293   0.0126290   -1.356    0.175
## intercept  0.0001937   0.0001412    1.372    0.170
## 
## Fit:
## sigma^2 estimated as 0.0001249,  Conditional Sum-of-Squares = 0.78,  AIC = -38545.42
plot(AR_1)

AR_3= arma(Retornos$Retornos,order = c(3,0))
summary(AR_3)
## 
## Call:
## arma(x = Retornos$Retornos, order = c(3, 0))
## 
## Model:
## ARMA(3,0)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0913071 -0.0051968  0.0002461  0.0055609  0.0927998 
## 
## Coefficient(s):
##             Estimate  Std. Error  t value Pr(>|t|)    
## ar1       -0.0214857   0.0126013   -1.705   0.0882 .  
## ar2       -0.0495730   0.0125885   -3.938 8.22e-05 ***
## ar3       -0.0669798   0.0126012   -5.315 1.06e-07 ***
## intercept  0.0002204   0.0001407    1.566   0.1173    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Fit:
## sigma^2 estimated as 0.000124,  Conditional Sum-of-Squares = 0.78,  AIC = -38584.02
plot(AR_3)

AR_4= arma(Retornos$Retornos,order = c(4,0))
summary(AR_4)
## 
## Call:
## arma(x = Retornos$Retornos, order = c(4, 0))
## 
## Model:
## ARMA(4,0)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0888419 -0.0052396  0.0001993  0.0055609  0.0923756 
## 
## Coefficient(s):
##             Estimate  Std. Error  t value Pr(>|t|)    
## ar1       -0.0195092   0.0126250   -1.545  0.12228    
## ar2       -0.0482138   0.0125986   -3.827  0.00013 ***
## ar3       -0.0662641   0.0125982   -5.260 1.44e-07 ***
## ar4        0.0301457   0.0126247    2.388  0.01695 *  
## intercept  0.0002141   0.0001407    1.522  0.12806    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Fit:
## sigma^2 estimated as 0.0001239,  Conditional Sum-of-Squares = 0.78,  AIC = -38586.89
plot(AR_4)