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August 28, 2024 19:48
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| # 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]) |
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