Rolling annualized mean and volatility
In previous exercises, you have already familiarized yourself with the Return.annualized() and StdDev.annualized() functions. In this exercise, you will also use the function SharpeRatio.annualized() to calculate the annualized Sharpe Ratio. This function takes the arguments R, and Rf. The R argument takes an xts, vector, matrix, data.frame, timeSeries, or zoo object of asset returns. The Rf argument is necessary in SharpeRatio.annualized(), as it takes into account the risk-free rate in the same period of your returns. For this example, you can use the object rf to fulfill the Rf argument.
The function chart.RollingPerformance() makes it easy to visualize the rolling estimates of performance in R. Familiarize yourself first with the syntax of this function. It requires you to specify the time series of portfolio returns (by setting the argument R), the length of the window (width), and the function used to compute the performance (argument FUN). To see all three plots together, PerformanceAnalytics provides a shortcut function charts.RollingPerformance(). Note the charts instead of chart. This function creates all of the previous charts at once and does not use the argument FUN!
This is a part of the course
“Introduction to Portfolio Analysis in R”
Exercise instructions
- Calculate the annualized returns, volatility, and Sharpe Ratio for
sp500_returns. Assign these values toreturns_ann,sd_ann, andsharpe_annrespectively. Remember to supply the risk-free rate to theRfargument when calculating the Sharpe Ratio. - We provided the code for a plot of a rolling 12-month estimate of the annualized mean. Use this to help with the other plots!
- Plot the rolling 12-month estimates of the annualized volatility of the S&P 500 returns.
- Plot the rolling 12-month estimates of the annualized Sharpe ratio of the S&P 500 returns.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the mean, volatility, and Sharpe ratio of sp500_returns
returns_ann <- ___
sd_ann <- ___
sharpe_ann <- ___
# Plotting the 12-month rolling annualized mean
chart.RollingPerformance(R = sp500_returns, width = 12, FUN = "Return.annualized")
# Plotting the 12-month rolling annualized standard deviation
___
# Plotting the 12-month rolling annualized Sharpe ratio
___This exercise is part of the course
Introduction to Portfolio Analysis in R
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
The history of portfolio returns reveals valuable information about how much the investor can expect to gain or lose. This chapter introduces the R functionality to analyze the investment performance based on a statistical analysis of the portfolio returns. It includes graphical analysis and the calculation of performance statistics expressing average return, risk, and risk-adjusted return over rolling estimation samples.
Exercise 1: Dimensions of portfolio performanceExercise 2: Exploring the monthly S&P 500 returnsExercise 3: The monthly mean and volatilityExercise 4: The (annualized) Sharpe ratioExercise 5: Excess returns and the portfolio's Sharpe ratioExercise 6: Annualized mean and volatilityExercise 7: Time-variation in portfolio performanceExercise 8: Effect of window length choiceExercise 9: Rolling annualized mean and volatilityExercise 10: Subperiod performance analysis and the function windowExercise 11: Non-normality of the return distributionExercise 12: Balancing risk and rewardExercise 13: Detecting non-normality using skewness and kurtosisExercise 14: Downside risk measuresExercise 15: Drawdowns due to buying high, selling lowWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.