get_data_and_compute_log_returns.R

# Set tickers
tickers <- c('MSFT', 'AAPL', 'TSLA', 'NFLX', 'META', 'AMZN','GOOGL')

# Set start and end dates
start = "1990-01-01"
end = "2024-08-01"

df <- new.env()

# Import the data
getSymbols(tickers,src='yahoo',env = df, from=start,to=end,auto.assign=TRUE)

# Create dataframe for stock close prices using the Apple date column
df2 <- data.frame(date = index(df[[ls(df)[1]]]))

# Loop to join all the stock close price data in a single dataframe
for (ticker in tickers) {
  # Save the adjusted close price in a temporary dataframe called temp_df
  temp_df <- data.frame(coredata(fortify.zoo(Ad(df[[ticker]]))))
  # Rename the columns
  colnames(temp_df) <- c('date',ticker)
  # Save the adjusted close price in df2
  df2 <- left_join(df2, temp_df[c('date',ticker)], by=c("date"))
}

# Drop the NaN values
df2 <- na.omit(df2)

# Create the returns dataframe
var_data <- data.frame(date=df2$date[-1])

# Loop to create the log returns
for(ticker in tickers) {
  # Compute the log returns for each asset
  var_data[[ticker]] <- log(df2[[ticker]][-1]/df2[[ticker]][-length(df2[[ticker]])])
}

# Create a dataframe to select the returns and not the date column
data <- var_data[,match(tickers,colnames(var_data))]

# Convert the dataframe into a matrix for estimation purposes
tvp_var_data <- as.matrix(var_data[,2:8])