What is Asymmetrical Distribution
Asymmetrical distribution is a situation in which the values of variables occur at irregular frequencies and the mean, median and mode occur at different points. An asymmetric distribution exhibits skewness. In contrast, a Gaussian or normal distribution, when depicted on a graph, is shaped like a bell curve and the two sides of the graph are symmetrical.
BREAKING DOWN Asymmetrical Distribution
Investors should care about how investment return data is distributed. Asset classes (stocks, bonds, commodities, currencies, real estate, etc.), sectors within those asset classes (e.g., technology, healthcare, staples, etc.), as well as portfolios comprising combinations of these asset classes are all subject to various return distributions. Empirically, they follow asymmetric distribution patterns. This is because investment performance is often skewed by periods of high market volatility or unusual fiscal and monetary policies during which returns can be abnormally high or low.
The departure from "normal" returns has been caused with more frequency in the last two decades, beginning with the internet bubble of the late 1990s, September 11 terrorist attacks, the housing bubble and subsequent financial crisis, and years of quantitative easing, which came to an end in 2017. The unwinding of the Federal Reserve Board's unprecedented easy monetary policy may be the next chapter of volatile market action and more asymmetrical distribution of investment returns.
Better Asset Allocation Models
Given that disruptive events and extraordinary phenomena occur more often than expected, asset allocation models can be improved by incorporating asymmetrical distribution assumptions. Traditional mean-variance frameworks developed by Harry Markowitz were based on assumptions that asset class returns are normally distributed. Traditional asset allocation models work well in persistent "normal" market environments. However, they may not protect portfolios from severe downside risks when markets become abnormal. Modeling with asymmetric distribution assumptions can help reduce volatility in portfolios and reduce capital loss risks.