Tarea 2
José Roberto del Valle
2022-07-29
Regístrate en la página de FRED, consigue una llave y descarga la variable ‘VIXCLS’. Para mostrar tus
resultados imprime el nombre de la variable a la que se asigna la descarga. Nota: el id de esta variable es VIXCLS y es el índice de volatilidad VIX).
knitr::opts_chunk$set(echo = TRUE)
library(fredr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggthemes)
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(ggplot2)
library(tidyr)
fredr_set_key("7a7bbb3b1c59ac799cb516deafec8600")VIX <- fredr(
series_id = "VIXCLS",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)# Construye una matriz de datos financieros y económicos que incluya:
#• Nivel de la tasa de referencia de Estados Unidos.
tasa_referencia <- fredr(
series_id = "FEDFUNDS",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
# • Nivel de tasas de interés en Estados Unidos con los siguientes vencimientos: 1m, 3m, 6m, 1y, 2y, 3y, 5y,7y, 10y, 20y y 30y.
rate1m <- fredr(
series_id = "DGS1MO",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate3m <- fredr(
series_id = "DGS3MO",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate6m <- fredr(
series_id = "DGS6MO",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate1y <- fredr(
series_id = "DGS1",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate2y <- fredr(
series_id = "DGS2",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate3y <- fredr(
series_id = "DGS3",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate5y <- fredr(
series_id = "DGS5",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate7y <- fredr(
series_id = "DGS7",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate10y <- fredr(
series_id = "DGS10",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate20y <- fredr(
series_id = "DGS20",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
rate30y <- fredr(
series_id = "DGS30",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25")
)
# • Nivel de la tasa hipotecaria de 30 años en E.E.U.U.
Tasa_hipotecaria <- fredr(
series_id = "MORTGAGE30US",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
# • Nivel de los ‘breakeven de inflación’ de 5 y 10 años de E.E.U.U.
breakeven_inflacion_5y <- fredr(
series_id = "T5YIE",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
breakeven_inflacion_10y <- fredr(
series_id = "T10YIE",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
# • Nivel de la inflación forward de 5 años en 5 años de E.E.U.U.
inflacion_forward_5_5 <- fredr(
series_id = "T5YIFR",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
# • Nivel de la tasa de interés real en Estados Unidos-obtenida de bonos bursátiles- con vencimientos de 5 y 10 años;
tasa_interes_real5Y <- fredr(
series_id = "DFII5",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
tasa_interes_real10Y <- fredr(
series_id = "DFII10",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
# # Diferencial entre los siguientes tipos de deuda y bonos gubernamentales en divisa local: alto rendimiento en E.E.U.U., grado de inversión en E.E.U.U., alto rendimiento en Europa y alto rendimiento en mercados emergentes. Nota: trata de no obtener varios vencimientos y busca un indicador que sea un resumen (ej. un promedio de distintos vencimientos).
alto_rendimiento_en_EEUU <- fredr(
series_id = "BAMLH0A2HYB",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
grado_inversion_en_EEUU <- fredr(
series_id = "BAMLC0A0CM",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
alto_rendimiento_europa <- fredr(
series_id = "BAMLHE00EHYIOAS",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
alto_rendimiento_mercados_emergentes <- fredr(
series_id = "BAMLEMHBHYCRPIOAS",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
# Nivel de las principales referencias de petróleo (WTI, Brent).
wti <- fredr(
series_id = "DCOILWTICO",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
brent <- fredr(
series_id = "DCOILBRENTEU",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
#Tasa de desempleo
tasa_desempleo <- fredr(
series_id = "UNRATE",
observation_start = as.Date("2003-01-02"),
observation_end = as.Date("2022-07-25"))
lista <- as.list(.GlobalEnv)
lista_obj <- as.vector(names(lista))
for (i in 1:length(lista)){
abc <- get(lista_obj[i])
abc <- abc %>% select(colnames(abc)[1],colnames(abc)[2],colnames(abc)[3])
colnames(abc)<- c("date", paste0("series_id_",abc[1,2]) ,lista_obj[i])
abc <- abc[-2]
assign(lista_obj[i], abc)
}
rm(abc,i)
matriz <- get(lista_obj[1])
for (i in 2:length(lista_obj)){
matriz <- matriz %>% full_join(get(lista_obj[i]),
by = "date")
}“# # Para mostrar tus resultados imprime los siguientes comandos: print(tu_matriz_de_datos) print(tu_matriz_de_datos[8000:8050,]) print(tail(tu_matriz_de_datos))
print(head(matriz))## # A tibble: 6 × 27
## date tasa_int…¹ rate2y brent rate3m grado…² rate1m break…³ rate10y VIX
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2003-01-02 2.43 1.8 30.3 1.22 1.86 1.18 1.64 4.07 25.4
## 2 2003-01-03 2.43 1.79 31.4 1.22 1.85 1.18 1.62 4.05 24.7
## 3 2003-01-06 2.46 1.84 31.4 1.21 1.82 1.17 1.63 4.09 24.9
## 4 2003-01-07 2.42 1.77 30.8 1.19 1.81 1.17 1.62 4.04 25.1
## 5 2003-01-08 2.29 1.71 29.3 1.19 1.8 1.17 1.71 4 25.5
## 6 2003-01-09 2.41 1.87 30.3 1.2 1.77 1.18 1.78 4.19 24.2
## # … with 17 more variables: rate30y <dbl>, inflacion_forward_5_5 <dbl>,
## # rate7y <dbl>, rate5y <dbl>, rate3y <dbl>, rate1y <dbl>, rate6m <dbl>,
## # wti <dbl>, alto_rendimiento_en_EEUU <dbl>, tasa_referencia <dbl>,
## # Tasa_hipotecaria <dbl>, alto_rendimiento_europa <dbl>,
## # tasa_interes_real5Y <dbl>, tasa_desempleo <dbl>,
## # alto_rendimiento_mercados_emergentes <dbl>, rate20y <dbl>,
## # breakeven_inflacion_5y <dbl>, and abbreviated variable names …
## # ℹ Use `colnames()` to see all variable namesprint(matriz[8000:8050,])## # A tibble: 51 × 27
## date tasa_intere…¹ rate2y brent rate3m grado…² rate1m break…³ rate10y VIX
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA
## # … with 41 more rows, 17 more variables: rate30y <dbl>,
## # inflacion_forward_5_5 <dbl>, rate7y <dbl>, rate5y <dbl>, rate3y <dbl>,
## # rate1y <dbl>, rate6m <dbl>, wti <dbl>, alto_rendimiento_en_EEUU <dbl>,
## # tasa_referencia <dbl>, Tasa_hipotecaria <dbl>,
## # alto_rendimiento_europa <dbl>, tasa_interes_real5Y <dbl>,
## # tasa_desempleo <dbl>, alto_rendimiento_mercados_emergentes <dbl>,
## # rate20y <dbl>, breakeven_inflacion_5y <dbl>, and abbreviated variable …
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesprint(tail(matriz))## # A tibble: 6 × 27
## date tasa_int…¹ rate2y brent rate3m grado…² rate1m break…³ rate10y VIX
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-08-01 NA NA NA NA NA NA NA NA NA
## 2 2020-11-01 NA NA NA NA NA NA NA NA NA
## 3 2021-05-01 NA NA NA NA NA NA NA NA NA
## 4 2021-08-01 NA NA NA NA NA NA NA NA NA
## 5 2022-01-01 NA NA NA NA NA NA NA NA NA
## 6 2022-05-01 NA NA NA NA NA NA NA NA NA
## # … with 17 more variables: rate30y <dbl>, inflacion_forward_5_5 <dbl>,
## # rate7y <dbl>, rate5y <dbl>, rate3y <dbl>, rate1y <dbl>, rate6m <dbl>,
## # wti <dbl>, alto_rendimiento_en_EEUU <dbl>, tasa_referencia <dbl>,
## # Tasa_hipotecaria <dbl>, alto_rendimiento_europa <dbl>,
## # tasa_interes_real5Y <dbl>, tasa_desempleo <dbl>,
## # alto_rendimiento_mercados_emergentes <dbl>, rate20y <dbl>,
## # breakeven_inflacion_5y <dbl>, and abbreviated variable names …
## # ℹ Use `colnames()` to see all variable names# Para mostrar tus resultados grafica-en elementos separados-la serie del UST 2y (usa verde) y de la tasa de
desempleo (usa rojo). Nota: en tu gráfica deben ser claras las unidades, los elementos de cada eje y debes usar los colores indicados.
## Warning: Removed 1 row(s) containing missing values (geom_path).
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