Note: This article appears on the ETFtrends.com Strategist Channel
By Giralda Advisors
In prior installments of our series on Risk‐Managed Investing (RMI — see archive here), we described how embedding volatility dampening and/or downside risk mitigation directly within the equity investment itself can help solve difficult and pressing problems in portfolio construction and financial planning.
In our most recent piece, “Risk‐Managed Investing and Portfolio Optimization,” we illustrated the significant positive effect RMI could have on the portfolio’s risk/return profile, i.e., by raising the “efficient frontier.” As is typically the case with leading‐edge strategies, portfolio risk/return metrics and benchmarks are not yet fully equipped to capture and measure the benefits of RMI. One reason for this is that successful RMI strategies exhibit “convexity” which, in this context, means that they allow more upside than downside participation in equity markets, all for a cost of some sort. Most traditional industry metrics do not effectively handle this bifurcated behavior, nor do they focus on whether the cost is sufficiently low. Let’s examine some specific metrics.
Risk‐Based Measures
A number of approaches attempt to measure the volatility of an investment. The rudimentary metric for volatility is standard deviation, a measure of the degree to which an investment’s series of short-term (e.g., daily, weekly, monthly) returns tends to deviate from its long‐term average value. Standard deviation does not distinguish between risk that is inherent to the market and cannot be diversified away (“systematic risk”) and risk that is peculiar to the specific investment under review (“idiosyncratic risk”).
A statistic that does attempt to make that distinction and isolate idiosyncratic risk is beta. But beta is an accurate measure only for investments that behave in a way consistent with the assumptions of the Capital Asset Pricing Model (returns must follow a symmetric normal probability distribution, and the relationship of the investment to its benchmark must be linear, to name a few) that are generally quite far from reality. This limits beta’s reliability for investments that feature the convexity mentioned above.
A drawback common to both standard deviation and beta is that neither attempts to differentiate between upside (good) volatility and downside (bad) volatility. So‐called downside standard deviation represents an effort to focus on the bad volatility, but it still suffers from the other issues cited for standard deviation. Another statistic that is focused on downside risk is maximum drawdown (MDD), which measures the degree to which an index drops from a prior peak value.
One issue common to MDD and all the preceding metrics is that they are one‐dimensional — they do not attempt to capture the expected return that accompanies the measured risk. And none of these statistics has a “threshold value” to demarcate an acceptable level of risk.
Risk‐Adjusted Return‐Based Measures
There is an abundance of approaches that attempt to respond to the question of whether the investment under review is delivering an attractive return relative to the risk it is taking.
The Sharpe Ratio, Treynor Ratio, and Sortino Ratio, for example, each have as their numerator “excess return” (i.e., the difference between the investment’s return and a target return such as the return of a risk‐free investment) but differ in the risk metric used in the denominator — respectively, standard deviation, beta, and downside standard deviation. The Calmar Ratio divides the investment’s return by its MDD. The Sterling Ratio is a close cousin to the Calmar Ratio, but employs average annual drawdown (and a ballast term).
As was the case with the risk‐based measures in the preceding section, none of these statistics has a threshold value above which an acceptable level of risk‐adjusted return can be identified. Thus, the metrics in this section typically are used to compare one investment against another. However, it is important to note that implicit in all these measures is the notion that there is a simple, linear “exchange rate” between a unit of risk and a unit of return. This is a greatly idealized depiction of reality and, therefore, these metrics can lead to incorrect conclusions if applied blindly.
More Useful Approaches
One measure that can be quite relevant to RMI strategies is Upside/Downside Capture Ratio, which compares the degree to which the investment tracks a market index (e.g., the S&P 500 Stock Index) on its way up to the degree it tracks it on the way down. However, there is a practical limitation to this ratio, as well as with all the metrics thus far discussed, when applied to promising new investments. To be truly meaningful, they should be calculated using data that spans at least one, but preferably more than one, full market cycle. Many investments worth considering have come to market since the crash of 2008‐09, and have therefore existed during only the recovery portion of the cycle. MDD, for example, and even the upside/downside capture ratio are largely useless in this situation.
One way to gauge the efficacy of an RMI strategy that has existed during only the upward phase of a market cycle is to compare it against a benchmark index designed expressly for RMI applications. The Chicago Board Options Exchange publishes several such indexes — the CBOE S&P 500 5% Put Protection Index (PPUT), the CBOE S&P 500 95‐110 Collar Index (CLL), and the CBOE VIX Tail Hedge Index (VXTH) — which were described in our earlier piece Marketplace Review of Risk‐Managed Investments. While awaiting a bear market or market crash to provide a live test of the downside protection afforded by recent‐vintage RMI strategies, these benchmarks provide a very useful way to determine whether the cost of that protection is acceptably low.
There is another approach to even more explicitly assess the cost‐effectiveness of RMI strategies absent a significant market downturn. In our article The Tolerable Cost of Risk‐Managed Investing, we outline a practical way to measure that cost and provide a set of threshold values against which to compare it. This directly addresses the drawbacks in the existing metrics noted above. We believe an approach such as this could form the basis for the investment performance measurement tools of the future, when RMI strategies become even more prominent and important than they are now.
This article was written by Jerry Miccolis, Gladys Chow and Rohith Eggidi of Giralda Advisors, a participant in the ETF Strategist Channel.
[related_stories]Disclosure Information
This material is for informational purposes only. Nothing in this material is intended to constitute legal, tax, or investment advice. Investing involves risk including potential loss of principal.
Giralda Advisors, located in New York City, is an asset management firm that focuses on providing riskmanaged exposure to the equity markets with a goal of limiting asset depreciation during both protracted and catastrophic market downturns while allowing substantial asset appreciation in uptrending markets. The Giralda Advisors team welcomes your inquiries. Please call (212) 235‐6801 or visit us at http://www.giraldaadvisors.com/.