Tail risk

Tail risk, sometimes called "fat tail risk," is the financial risk of an asset or portfolio of assets moving more than three standard deviations from its current price, above the risk of a normal distribution. Tail risks include low-probability events arising at both ends of a normal distribution curve, also known as tail events. However, as investors are generally more concerned with unexpected losses rather than gains, a debate about tail risk is focused on the left tail. Prudent asset managers are typically cautious with the tail involving losses which could damage or ruin portfolios, and not the beneficial tail of outsized gains. The common technique of theorizing a normal distribution of price changes underestimates tail risk when market data exhibit fat tails, thus understating asset prices, stock returns and subsequent risk management strategies. Tail risk is sometimes defined less strictly: as merely the risk (or probability) of rare events. The arbitrary definition of the tail region as beyond three standard deviations may also be broadened, such as the SKEW index which uses the larger tail region starting at two standard deviations. Although tail risk cannot be eliminated, its impact can be somewhat mitigated by a robust diversification across assets, strategies, and the use of an asymmetric hedge. Traditional portfolio strategies rely heavily upon the assumption that market returns follow a normal distribution, characterized by the bell curve, which illustrates that, given enough observations, all values in a sample will be distributed symmetrically with respect to the mean. The empirical rule then states that about 99.7% of all variations following a normal distribution lies within three standard deviations of the mean. Therefore, there is only a 0.3% chance of an extreme event occurring. Many financial models such as Modern Portfolio Theory and Efficient Markets assume normality. However, financial markets are not perfect as they are largely shaped by unpredictable human behavior and an abundance of evidence suggests that the distribution of returns is in fact not normal, but skewed.