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Risk measurement is a critical part of investing because it allows you to quantify the level of uncertainty in your portfolio. To that end, portfolio managers rely on several risk metrics when making asset allocation or security selection decisions.
When it comes to investing, risk can be quantified in several ways, with the most common measurements being standard deviation, Sharpe ratio, and beta. These and other measurements can be further broken down into two categories:
In each case, risk measures quantify uncertainty of an investment outcome over a specific time period. Let us take a deeper look into these risk metrics.
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Absolute risk metrics measure the risk of financial assets in absolute terms, i.e., not in relation to other assets or market returns. They include:
Portfolio Standard Deviation
Portfolio standard deviation is one of the most widely used metrics for evaluating risk. Mathematically, it refers to the extent of dispersion for a population of observations. This measure provides valuable insight if your goal is to look at investment return volatility.
However, if you want to determine whether returns are below average, standard deviation alone is insufficient.
For instance,the Vanguard Total Stock Market Index Admiral (VTSAX), 1290 Convertible Securities A (TNFAX) and T. Rowe Price Dividend Growth (PRDGX) all have a standard deviation above 10. Compared to these, Fidelity GNMA (FGMNX) has a standard deviation of 2.19, which implies greater stability.
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The value-at-risk metric examines the potential of extreme loss in the value of a portfolio over a certain timeframe and for a given level of confidence. VaR is calculated using a specified level of loss, a time period covering the risk assessment and a confidence interval.
Typically, confidence intervals are set at either 1% probability or 5% probability, which are in turn called 99% or 95% VaR, respectively. If your portfolio has a one-day 95% VaR of $100,000, there’s a 5% probability that the portfolio will lose more than $100,000 over a one-day period.
A shortfall risk is the likelihood that an investment’s value will be less than what is needed to meet the portfolio’s objectives.
Using data simulation, shortfall risk may determine, for example, that an investment portfolio has a 30% chance of being exhausted before the end of the liability funding period.
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Relative risk metrics measure volatility and the comparable risk of potential investments relative to the broader market. They include:
When it comes to investing, tracking error measures the standard deviation of excess returns compared with a common benchmark. In other words, it measures the volatility of excess returns of a security relative to a benchmark. For example, a fund with a tracking error of 10 basis points relative to its benchmark should return between 9.9% and 10.1% annually if the benchmark returns 10% each year.
The Sharpe ratio represents the risk-adjusted return of a portfolio. In other words, it measures how much return is being earned for each unit of risk assumed.
If the ratio is negative, it means the portfolio underperforms risk-free assets, such as a U.S. Treasury bill.
For instance, VTSAX, TNFAX, PRDGX and FGMNX all have positive Sharpe ratios, which means they outperform safe-haven assets like government bonds. Although FGMNX barely outperforms risk-free assets, it has a better rate of return than the broader U.S. Intermediate Government Bond category, which has a negative Sharpe ratio.
The information ratio is another risk metric used by portfolio managers to analyze the risk-adjusted return of an investment versus a benchmark.
It is calculated by dividing an asset’s excess return by
its tracking error relative to the benchmark.
Beta is another commonly used risk ratio that measures volatility of an investment relative to the market as a whole.
It is expressed as a positive integer, with 1 representing a perfect match between security and the overall market’s performance. Any number above that indicates that the investment in question will have greater volatility than the overall market.
For instance, PRDGX and FGMNX have betas that are lower than 1, which means they are more stable than the overall market. VTSAX’s performance is virtually in line with the broader market since its beta is 1.02. With a beta of 1.36, TNFAX varies more significantly.
The Treynor ratio measures the risk-adjusted return of a portfolio relative to the overall market.
It essentially compares an investment’s excess return relative to a risk-free asset (i.e., U.S. Treasury) divided by the asset’s beta. This metric is only useful if we assume that the portfolio manager has accounted for unsystematic risk (i.e., company-level risk).
With that in mind, traditional risk metrics rely on historical performance indicators to determine the best estimates of future risks. This leaves them exposed to several pitfalls related to data quality, time-period bias and manager-selection bias.
Regarding data quality, every portfolio manager will tell you that past performance is no guarantee of future success. Not every metric will yield useful results, especially when evaluating likely outcomes.
One of the biggest drawbacks of traditional risk metrics is time-period bias, which is a sampling error that accounts for only a certain set of circumstances. This could easily lead to inaccurate results since the conclusion is obtained from insufficient data.
Much like time-period bias, manager-selection bias is a kind of tracking error that occurs when a portfolio manager decides what is going to be studied. Once again, the choice of time period and set of circumstances covered may lead to inaccurate conclusions.
Be sure to check the best practices to construct your portfolio here.
Risk metrics are imperfect, but widely used strategies for evaluating portfolio risk. Used alongside other portfolio management strategies, they can help portfolio managers factor systemic and non-systemic risks in their investment decisions.
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