Estimating Downside Risk Effectively

More and more insurance producers are moving toward planning, specifically through the registered investment advisor route. For these advisors, the big question is not necessarily which portfolio should the client be invested in with all of their money, it’s how much of the client’s money belongs in investments, and how much money should be used to support basic needs with insurance products like annuities. These advisors can and should bridge the gap between the investment word and the insurance world.

Weighing Options
For instance, if a client has a $1,000,000 portfolio that consists of 50% stocks and 50% bonds, their downside risk exposure is roughly $350,000. So, if the client can’t get through retirement with $650,000, then they should probably be invested differently in the first place. That’s the question of risk capacity. Alternatively, if the same client has $500,000 in a fixed annuity and the remaining $500,000 in the market, the potential loss is $175,000 rather than $350,000. While the annuity won’t potentially drive the same long-term returns, it may be beneficial because of its principal protection and lack of correlation to market moves. If the client can get through retirement with $825,000, after accounting for the lower potential returns, then incorporating an annuity into the client’s overall plan is likely a better path.

Rather than changing the portfolio’s blend of stocks and bonds, retirement income advisors are increasingly considering how much is allocated to investments in the first place and how much is allocated to savings and insurance vehicles designed to guarantee a minimum level of income or return depending on product and case design. This is an example of using annuities to better manage client portfolios to the client’s risk capacity.

Risk Tolerance
A second consideration is risk tolerance, which could be thought of as the maximum loss a client can handle before they capitulate and sell for fear that their investments could go down even further. Ideally, an advisor would never put a client in a portfolio that exceeds the client’s risk tolerance because even though it’s likely that the client would achieve higher long term returns in a more aggressive portfolio, if the client sells during inevitable dips, those higher long term returns would not be realized.

In order to effectively plan for both risk tolerance and risk capacity, an advisor must have a good estimate of the downside exposure that is actually present in a client portfolio. Unfortunately, most risk software used by advisors doesn’t adequately capture the inherent risk in financial markets, primarily due to the methodology of these tools. Most risk estimates use a value at-risk methodology, coupled with a normal distribution (also known as a Gaussian distribution, or a bell curve). This methodology suggests that 95% of market returns fall within two standard deviations of the mean. While it’s the most common way of representing risk, markets don't seem to fit this model very well. In fact, extreme market events happen with greater frequency and severity than the normal bell curve would predict. For instance, the bell curve would suggest that Black Monday should actually happen once every 100,000 times the age of the universe.

Dangerous Curve Ahead
In his New York Times best seller, The Black Swan: The Impact of the Highly Improbable, Nicholas Nassim Taleb highlighted a decades-old idea that financial markets do not follow a bell curve. In fact, he suggested that using a bell curve as the basis for navigating risk can be dangerous. This forced many major financial institutions to adopt heavy-tailed risk models that more accurately capture the frequency and impact of much more severe losses in financial markets.

Heavy-tailed distributions approach zero at a slower rate and can have outliers with very high values. They better represent the actual risk in markets. The heavy-tailed model underestimated risk in only 2.4% of cases.* However, in most consumer-level conversations about risk, heavy-tailed risk models have yet to become commonplace. Despite the fact that if you compare the standard risk estimates that use a normal distribution and value at risk methodology to a heavy-tailed model using an estimated tail loss methodology, and apply it to all listed U.S. securities, you’ll find that the common model underestimated risk in over 77.3% of cases.

Heavy-Tailed Model
Using rolling six-month periods of SPY returns through May 7, 2020. The common risk estimate would suggest a risk of 14.64%, while the heavy-tailed model discussed above would suggest a risk of 51.79%. The worst six-month period during the financial crisis was -45%, and there were 132 rolling six-month periods during that time. The average of those periods exceeding the common risk estimate was -33%, more than double that common methodology. The 2020 pandemic is another great example. The worst six-month period was -24% (as of May 7, 2020), and the average of the 17 periods in excess of the common estimate was -18%.

A good risk estimate that instills confidence and tempers irrational fear should be based on a heavy-tailed model and an estimated tail loss methodology. Using outdated or oversimplified risk metrics leads to doubt and panic during market volatility. And of course, the last thing you want to happen when the market dips, is for your client to panic and sell at the worst time, locking in a loss. A good advisor should help clients avoid those behavioral mistakes by using quality risk metrics to effectively estimate downside risk and prepare for those big market swings, while building client portfolios that address both risk tolerance and risk capacity.

Joe Elsasser, CFP is the founder and president of Covisum®, a financial tech company focused on creating software that improves lives through better financial decisions. Covisum helps financial advisors serving mass-affluent clients in or near retirement and powers some of the nation’s largest financial planning institutions.

*Piccinini, R. (2017, March). Remodeling Portfolio Risk Modeling, Financial Service Professionals Newsletter.