Gestão de Risco

 UNIVERSIDADE FEDERAL DA PARAÍBA

remove(list=ls())
par(mfrow=c(1,1))
options(scipen=999)
options(max.print = 100000)
require(fPortfolio)
library(tseries)
library(tidyverse)
library(fBasics)
library(knitr)
library(xts)
library(kableExtra)

Definindo o Recorte Temporal da Serie

# Intervalo de Tempo dos dados
dataini <- "2024-03-07"
datafim <- "2024-07-17"

Criando a Tabela de Indicadores

# Lista de Empresas que serão Requeridas
empresas <- list(vivo = "vivt3.sa", 
                 totvs = "tots3.sa", 
                 weg = "wege3.sa", 
                 tim = "tims3.sa", 
                 localiza = "rent3.sa", 
                 transmissao_paulista = "trpl3.sa", 
                 cemig = "cmig3.sa",
                 engie = "egie3.sa", 
                 taesa = "taee3.sa", 
                 cpfl = "cpfe3.sa", 
                 cyrela = "cyre3.sa", 
                 aliansce = "alos3.sa", 
                 ccr = "ccro3.sa", 
                 multiplan = "mult3.sa", 
                 eztc = "eztc3.sa", 
                 suzano = "suzb3.sa", 
                 vibra = "vbbr3.sa", 
                 klabin = "klbn3.sa", 
                 csn_mineracao = "cmin3.sa", 
                 gerdau = "ggbr3.sa",
                 slc = "slce3.sa",
                 fleury = "flry3.sa",
                 hypera = "hype3.sa",
                 minerva = "beef3.sa",
                 arezo = "arzz3.sa")

# Loop de busca para obter os dados e calcular os retornos
retornos <- lapply(empresas, function(ticker) {
  data <- na.omit(get.hist.quote(instrument = ticker, quote = "Close", start = dataini, end = datafim))
  diff(log(data))
})

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# Checando tamanho da amostra
length(retornos$vivo)

[1] 90

# Função para calcular indicadores
calcular_indicadores <- function(retorno) {
  risco <- sd(retorno) / mean(retorno)
  retorno_medio <- mean(retorno) * 100
  volatilidade <- (sd(retorno) * 100) * sqrt(252)
  list(risco = risco, retorno_medio = retorno_medio, volatilidade = volatilidade)
}

# Calcular indicadores para todas as empresas
indicadores <- lapply(retornos, calcular_indicadores)

# Criar data frame final
df2 <- data.frame(
  Empresas = c("Vivo", "Totvs", "Weg", "Tim", "Localiza", 
               "Transmissão Paulista", "Cemig", "Engie", "Taesa", "CPFL", "Cyrela", 
               "Aliansce", "CCR", "Multiplan", "EZTec", "Suzano", 
               "Vibra Energia", "Klabin", "CSN Mineração", "Gerdau", "Slc", "Fleury",
               "Hypera", "Minerva", "Arezo"),
  Retorno = sapply(indicadores, `[[`, "retorno_medio"),
  Risco = sapply(indicadores, `[[`, "risco"),
  Volatilidade = sapply(indicadores, `[[`, "volatilidade")
)

# Usar kable para criar a tabela e kableExtra para formatá-la
kable(df2[,-1], format = "html") |> 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) |> 
  row_spec(0, background = "#2166AC") |>   # Cabeçalho da tabela em azul
  row_spec(1:5, background = "#4393C3") |>    # Cabeçalho da tabela em azul
  row_spec(6:10, background = "#92C5DE") |>
  row_spec(11:15, background = "#D1E5F0") |> 
  row_spec(16:20, background = "#E7D4E8") |> 
  row_spec(21:25, background = "#FFAABB")

	Retorno	Risco	Volatilidade
vivo	-0.0765681	-19.536172	23.74586
totvs	-0.0576149	-27.977062	25.58803
weg	0.2612269	4.254763	17.64386
tim	-0.1120233	-14.489860	25.76753
localiza	-0.1643344	-11.318074	29.52578
transmissao_paulista	-0.0034041	-420.217698	22.70783
cemig	0.1736197	9.308681	25.65591
engie	0.1427441	8.682088	19.67355
taesa	-0.0199521	-38.835584	12.30038
cpfl	-0.0311073	-43.726992	21.59294
cyrela	-0.1617305	-12.859589	33.01560
aliansce	-0.0951991	-15.712669	23.74559
ccr	-0.0763152	-17.551065	21.26253
multiplan	-0.0489248	-31.329678	24.33241
eztc	-0.0728741	-32.775507	37.91601
suzano	-0.0917103	-25.558031	37.20882
vibra	-0.0550685	-30.824057	26.94592
klabin	0.0602318	20.871957	19.95673
csn_mineracao	-0.1221805	-19.074082	36.99525
gerdau	0.0299727	49.456179	23.53132
slc	0.0115442	128.714347	23.58791
fleury	0.0035442	379.010682	21.32425
hypera	-0.1385065	-14.161193	31.13654
minerva	0.0378357	62.457441	37.51340
arezo	-0.1059380	-18.091487	30.42471