Portfolio & Single Stock VAR and CVAR in R

Summary

In this tutorial, Dakota Wixom from QuantBros.com demonstrates how to calculate Value at Risk (VaR) and Conditional Value at Risk (CVaR) in R using libraries like Quantmod and PerformanceAnalytics. The video covers downloading stock data, calculating daily returns, and applying different risk calculation methods (historical, Gaussian, modified). Wixom explains how to assess the risk for individual stocks and portfolios, including visualizing the results with ggplot2. The tutorial also touches on advanced techniques such as portfolio risk contributions and how to use rolling windows for risk assessments, providing a comprehensive guide for financial analysis in R.

Takeaways

• 😀 Install and load key R libraries like Quantmod and PerformanceAnalytics for risk analysis.
• 😀 Use the `getSymbols` function to download stock data, specifically the adjusted close prices for calculation.
• 😀 Calculate daily returns with the `dailyReturn` function from Quantmod, simplifying the process of working with stock prices.
• 😀 Value at Risk (VaR) is calculated using the `VaR` function, allowing users to estimate potential losses at specified confidence levels.
• 😀 Conditional Value at Risk (CVaR), also known as expected shortfall, can be calculated similarly to VaR for a more comprehensive risk measure.
• 😀 VaR and CVaR can be calculated for individual stocks or an entire portfolio, with portfolio risk requiring a weight assignment for each stock.
• 😀 Portfolio risk analysis helps assess how individual stocks contribute to overall risk, as demonstrated with Microsoft, Apple, and Amazon.
• 😀 The Gaussian and Modified methods for calculating VaR provide different insights into risk based on data characteristics.
• 😀 Visualize the results using ggplot2 to compare VaR values for individual stocks and portfolios across different methods.
• 😀 Portfolio VaR tends to be lower than individual stock VaR due to diversification and the way covariance works within the portfolio.
• 😀 The script demonstrates how various risk metrics (VaR, CVaR) can be applied to financial portfolios, with an emphasis on their practical use in risk management.

Q & A

What are the two key risk metrics discussed in the video?

The two key risk metrics discussed in the video are Value at Risk (VAR) and Conditional Value at Risk (CVAR), also known as Expected Shortfall (ES).

Which libraries are loaded in the script to calculate VAR and CVAR?

The libraries loaded in the script are 'Quantmod' for downloading stock data and 'PerformanceAnalytics' for calculating returns and risk metrics.

What is the purpose of using the 'getSymbols' function in the script?

'getSymbols' is used to download historical stock price data, in this case, for Microsoft, to calculate VAR and CVAR based on stock returns.

How does the script calculate daily returns for Microsoft stock?

The script calculates daily returns using the 'dailyReturn' function from the 'Quantmod' package, which computes the percentage change in the stock price from one day to the next.

What is the significance of using different confidence levels (e.g., 95% and 99%) in VAR calculations?

The confidence levels determine the threshold for the potential loss. A 95% confidence level means the VAR calculation estimates the worst loss that will only occur in 5% of the worst cases, while a 99% confidence level provides a more conservative estimate.

How does the script calculate CVAR, and why is it higher than VAR?

CVAR is calculated by using the same 'historical' method as VAR, but it considers the average of the losses that occur beyond the VAR threshold. CVAR is generally higher than VAR because it captures the tail risk of extreme losses.

What is the process for calculating VAR and CVAR for a portfolio of multiple stocks?

The script first defines a portfolio with specified weights for each stock. It then uses the 'getSymbols' function to download price data for all the stocks and calculates the returns using the 'ROC' (rate of change) function from the 'TTR' library.

Why does the script use the 'Portfolio' method for VAR calculation instead of calculating VAR for individual stocks?

The 'Portfolio' method accounts for the correlation and interactions between the stocks, reflecting the overall risk of the portfolio rather than just the individual risks of each stock.

What is the difference between the 'Gaussian' and 'Modified' methods for calculating VAR?

The 'Gaussian' method assumes that returns are normally distributed, while the 'Modified' method, also known as the Cornish-Fisher method, adjusts for skewness and kurtosis, making it a better choice when returns are not normally distributed.

How does the script visualize the VAR and CVAR values for the portfolio and individual stocks?

The script uses the 'ggplot2' package to create a bar chart that compares the VAR values for the portfolio and individual stocks, color-coded by the stock ticker, allowing for a visual comparison of risk across different assets.