In our world, understanding the risk associated with an investment is crucial. Two commonly used metrics for risk assessment are standard deviation and maximum drawdown. While both provide valuable insights, their properties can differ significantly. This blog post aims to analyze the repeatability of these measurements using rolling timeframes across three major asset classes: the S&P 500 index (equities), the Barclays Aggregate Bond Index (bonds), and the Goldman Sachs Commodity Index (GSCI) (commodities).
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MEASURING RISK: STANDARD DEVIATION VS. MAXIMUM DRAWDOWN
Standard deviation (SD) measures the spread of returns around the average. A lower SD indicates less volatility and generally lower risk. However, SD does not capture extreme downside events.
Maximum drawdown (MDD) is the biggest drop in value an investment experiences from its highest to lowest point during a certain time. It shows how much you could lose in the worst case, but it doesn't tell you how often or how much the value usually goes up or down. Also, MDD often reflects rare, unpredictable events.
To see which of these measurements is more consistent, we use the coefficient of variation (CV). CV is found by dividing the standard deviation by the average return. It shows how much returns vary compared to the average. A smaller CV means the returns don't stray far from the average, indicating they are more stable.
ANALYSIS USING ROLLING TIMEFRAMES
To check how stable standard deviation (SD) and maximum drawdown (MDD) are in various market situations, we use rolling timeframes. This means we measure them over a set period, like 3 years, and then shift that period forward to measure again. This way, we can see how these risk measures change over time.
For each asset class, we analyze the average CV of SD and the average MDD across various rolling timeframes. We compare these values to assess which risk measurement exhibits greater consistency and repeatability.
RESULTS AND INTERPRETATION
The analysis reveals several key insights:
Equities (S&P 500): The average CV of SD consistently remains lower than the average MDD across all rolling timeframes. This suggests that the standard deviation provides a more consistent measure of risk for equities, as its relative volatility exhibits less variation over time compared to MDD.
Bonds (Barclays Aggregate): Both CV and MDD exhibit lower values compared to equities, reflecting the lower volatility of the bond market. However, the average CV of SD still remains consistently lower than the average MDD, indicating its superior repeatability.
Commodities (GSCI): The average CV of SD again demonstrates greater stability compared to MDD. While commodities tend to be more volatile than bonds, SD still provides a more reliable indicator of relative volatility over time.
These findings suggest that standard deviation, as measured by CV, offers a more repeatable measure of risk compared to maximum drawdown, especially for equities and bonds. This is because standard deviation (SD) reflects the total ups and downs in returns, both good and bad, while maximum drawdown (MDD) only looks at the biggest drop in value, which might not happen regularly over time..
CONCLUSION
This analysis offers useful information, but it's important to remember its limits. The choices of time periods and types of assets studied can affect the findings. Also, it's based on past data, and future market trends might differ from history.
Despite these limitations, the findings suggest that standard deviation, particularly when expressed as CV, provides a more consistent and informative measure of risk than maximum drawdown. This holds true for various asset classes and rolling timeframes.
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