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Diversification is defined as the amount of portfolio risk that is negated by holding assets that oset each other. In theory, each
asset class has a correlation to every other asset class. If the correlation between the two is negative, one goes up and the other goes down. Negatively correlated asset classes are thus said to diversify each other. Traditional measures of diversification are based upon these correlations.

The challenge with this method is that in times of market stress, correlations break down and many assets that are generally negatively correlated move together. Rather than the traditional method, we compare the risk of the portfolio, as a whole, to the combined risk of its individual components. In this way, we are able to help you understand how much of the risk in your portfolio is diversified away by holding its elements together.

We believe that starting with more accurate measures of risk helps set appropriate client expectations to manage investing behaviors, ultimately resulting in better retirement outcomes.


The End of an Era

In 1952, Harry Markowitz revolutionized the world of investing with the Modern Portfolio Theory. In this representation, investment risk is modeled as the standard deviation of returns, following a normal distribution, which is also known as a “bell curve."


Subsequently, virtually every model of financial returns, including those in your advisor’s planning and portfolio construction software, made the same assumption. According to these calculations, the odds of witnessing the crash of 1987 start with 54 leading zeros, odds so small they are meaningless. Yet, every single day, some stock price, somewhere, experience a sharp decline in the 5-20 standard deviation range. From a practical point of view, the use of normal distribution for modeling market returns is unacceptable.

More recently, with advances in computing power, better models for estimating risk have become available. These are generally referred to as “heavy-tailed” because they recognize that extreme events happen far more often than the traditional models would predict, and also have a far greater impact.

The chart above shows the difference between a heavy-tailed distribution and a normal distribution. Notice the red line has a steeper peak, recognizing that in normal markets, returns tend to be clustered near the average, but in extreme bull or bear markets, much bigger swings are possible than traditional models would predict.


Reasonable Expectations

A second flaw in the common methods of risk measurement is known as a Value at Risk (VaR) methodology. This method effectively draws a line at the 95th percentile of downside scenarios. This tells you the point at which you might consider yourself in a rare bear market, but it doesn’t tell you how bad the bear market could be.

Your advisor’s role is to help you stay on track when markets are rough. Understanding the likely extent of a bear market is central to that role. Models that incorporate an Estimated Tail Loss (ETL) method demonstrate the average downside of bad scenarios, rather than simply the edge of what could be considered a bad scenario. This perspective sets more accurate expectations of risk in portfolios, better preparing investors for the inevitable downturns.


We use SmartRisk software to identify the risk in your investment portfolio. SmartRisk uses a heavy-tailed model combined with a
focus on what happens in times of market stress. It helps us set reasonable expectations for you and identify any possible mismatches between the risk that is present in your portfolio and the risk that you are comfortable taking.


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