Working with Multiple Return Series in an xts Object using dailyReturn

In this article, we’ll delve into the world of time series analysis and explore how to use the dailyReturn function on an xts object that contains multiple return series. We’ll also discuss alternative approaches, including the use of the ROC function and leveraging the power of lapply and cbind.

Introduction

The dailyReturn function is a fundamental tool in time series analysis, allowing us to compute daily returns for a given xts object. However, when working with multiple return series within a single xts object, things can get complicated. In this article, we’ll take a closer look at how to use dailyReturn on an xts object with multiple return series and explore alternative approaches.

What are Return Series?

Before we dive into the world of daily returns, let’s define what return series are. A return series is a measure of the change in value of a time series over a specific period of time. In other words, it’s the difference between the current value and the previous value, expressed as a percentage.

For example, if we have an xts object representing the closing price of Intel (INTC) stock from 2007 to 2010, the return series might look like this:

The return series for INTC stock would be computed by taking the difference between each closing price and the previous one.

Using dailyReturn on an xts Object with Multiple Return Series

Let’s take a closer look at how to use dailyReturn on an xts object with multiple return series. We’ll start with a simple example using the built-in xts package in R.

# Load the required libraries
library(xts)
library(quantmod)

# Get the daily prices for INTC and IBM stocks from 2007 to 2010
a <- Cl(getSymbols("INTC", auto.assign = FALSE))
b <- Cl(getSymbols("IBM", auto.assign = FALSE))

# Merge the two data frames into a single xts object
a <- merge(a, b)

# Compute daily returns using dailyReturn
daily_return_INTC <- dailyReturn(a[, 1]) # Returns the result for only the first series (INTC)
daily_return_Intc <- apply(a, 2, dailyReturn) # Apply dailyReturn to all columns (i.e., both series)

As we can see from this example, using dailyReturn on an xts object with multiple return series is straightforward. However, there’s a catch: dailyReturn only returns the result for the first series in the data frame.

If you want to compute daily returns for all series in the data frame, you’ll need to use apply. As we can see from the code above, this can be done using lapply and cbind.

Using ROC Instead of DailyReturn

ROC (Return on Capital) is another popular function for computing return values. While it’s similar to dailyReturn in many ways, there are some key differences.

One major difference between ROC and dailyReturn is how they handle returns with missing values. ROC ignores missing values entirely, whereas dailyReturn computes a “missing” value as 0 (i.e., no change).

Another key difference is how they handle returns for the first row of the data frame. ROC uses the entire time series to compute the return, whereas dailyReturn uses only the last observation in the row.

# Compute daily returns using ROC
daily_return_ROC <- apply(a, 2, function(x) {
  if (length(x) > 1) {
    return((x[2] - x[1]) / x[1])
  } else {
    return(NA)
  }
})

Leveraging lapply and cbind

One of the most powerful features in R is its ability to apply functions across multiple columns (or rows, or even matrices) using lapply. We can use this feature to compute daily returns for all series in our xts object.

Here’s how you can do it:

# Compute daily returns using lapply and cbind
daily_return_lapply <- function(x) {
  if (length(x) > 1) {
    return((x[2] - x[1]) / x[1])
  } else {
    return(NA)
  }
}

daily_return_combined <- do.call(cbind, lapply(a, dailyReturn))

As we can see from this example, lapply and cbind allow us to compute daily returns for all series in our data frame with ease.

Handling Multiple Return Series

In the world of time series analysis, handling multiple return series is a common challenge. While dailyReturn provides an easy-to-use interface for computing daily returns, it only works well when dealing with a single series.

When working with multiple return series, we need to be mindful of how we compute and store these values. One common approach is to use separate data frames or matrices to hold the returns for each series.

For example:

# Create separate data frames for each series
daily_return_INTC <- apply(a[, 1], function(x) {
  if (length(x) > 1) {
    return((x[2] - x[1]) / x[1])
  } else {
    return(NA)
  }
})

daily_return_Intc <- apply(a, 2, function(x) {
  if (length(x) > 1) {
    return((x[2] - x[1]) / x[1])
  } else {
    return(NA)
  }
})

In this example, we use separate data frames to hold the returns for each series. This allows us to easily compute and store these values.

Conclusion

Working with multiple return series in an xts object can be a challenging task. While dailyReturn provides an easy-to-use interface for computing daily returns, it only works well when dealing with a single series.

By leveraging the power of lapply, cbind, and separate data frames or matrices, we can compute and store daily returns for multiple return series with ease.

Whether you’re working with a simple xts object or a complex one with multiple time series, understanding how to handle multiple return series is crucial for accurate analysis and modeling.

Last modified on 2024-08-18