By Salil Mehta, statistician and blogger at (Statistical Ideas)NEW YORK ( TheStreet) -- There is more risk in less-risky asset classes than one may think. Let's looks at the major equity and fixed-income asset classes, both in the U.S. as well as internationally. We'll look at two decades of data, from 1990 to 2010. A higher-order measure of risk, named kurtosis (also called the volatility of volatility), is designed to look at the relative thickness or thinness of the tail-ends of the distribution. A "higher-order" risk term looks at the largest swings in financial market performance, which is more of an emphasis on these large swings than what we would see if we only looked at the popular standard deviation measure. The aforementioned "distribution" is simply the collection of all of the performance results of an asset class over time. If we hear in the news that a stock had yesterday made a 52-week closing high, for example, then the distribution is the past 52 weeks of closing prices (of which yesterday's was the highest). Kurtosis can be used to look at the tail risk of an asset class, vs. what we would see if it were normally distributed. A normal distribution is a common probability distribution of things as they naturally occur. We'll look at how to calculate Kurtosis later. Many market participants simply use this distribution to look at technical analysis levels, or financial market returns. Evidence of this is when one speaks of financial market returns in terms of standard deviations, or risk measures such as value-at-risk, when talking about probabilities of an event occurring. However, this is the wrong way to think about financial market returns, as it is not a conservative enough of an assumption, and it underestimates the number of once-in-a-lifetime risk events that we have seen in the past decade. Now only some market participants know that financial market data do not follow a normal distribution, and even for those who do, it is a common mistake to then not throw out a common assumption about the underlying kurtosis of the return distributions. Then when we see kurtosis levels of, say four or five, for the risky assets (i.e., U.S. large value, non-U.S. developed stocks, U.S. large growth stocks, emerging market stocks), we know that there has been very heavy distribution in the tails. These risky assets include the SPDR S&P 500 Value ETF ( SPYV) and the SPDR S&P 500 Growth ETF ( SPYG). And while kurtosis doesn't distinguish between the upper tail and the lower tail, similar to the standard deviation measure, it should be noted that skew was negative for all of the asset classes shown here but for the non-U.S. bonds (for which skewness was virtually nonexistent).