What do fat tails mean in statistics?

What do fat tails mean in statistics?

By definition, fat tails are a statistical phenomenon exhibiting large leptokurtosis. This represents a greater likelihood of extreme events occurring similar to the financial crisis. Since the magnitude of fat tails are so difficult to predict, left tail events can have devastating effects on portfolio returns.

What does fatter tails mean in kurtosis?

Kurtosis measures the “fatness” of the tails of a distribution. Positive excess kurtosis means that distribution has fatter tails than a normal distribution. Fat tails means there is a higher than normal probability of big positive and negative returns realizations.

What is a tail analysis?

Long tail analysis is a method that graphs the relation between product demand, margin and variability, typically creating a long tail shaped graph.

How do you know if a distribution is fat-tailed?

A heavy tailed distribution has tails that are heavier than an exponential distribution (Bryson, 1974). In other words, the tails simply look fatter.

How do you describe the distribution of a tail?

The lower tail contains the lower values in a distribution. If you graph any distribution on a Cartesian plane, the lowest set of number will always appear on the left, because the lowest values on a number line are to the left. So, “lower tail” means the same thing as “left tail”.

What is a good kurtosis value?

A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable. Skewness: the extent to which a distribution of values deviates from symmetry around the mean.

How do you interpret skewness and kurtosis?

For skewness, if the value is greater than + 1.0, the distribution is right skewed. If the value is less than -1.0, the distribution is left skewed. For kurtosis, if the value is greater than + 1.0, the distribution is leptokurtik. If the value is less than -1.0, the distribution is platykurtik.

What is lower tail test?

In a lower-tailed test the decision rule has investigators reject H0 if the test statistic is smaller than the critical value. In a two-tailed test the decision rule has investigators reject H0 if the test statistic is extreme, either larger than an upper critical value or smaller than a lower critical value.

What is expected tail loss?

Expected Tail Loss (ETL), which is an extension of the com- monly used Value at Risk (VaR) statistic, fits these require- ments. Recall, VaR is a threshold statistic defined as the minimum amount of portfolio loss at a specified probability and horizon.

What is a fat tail in statistics?

The “fat tails” are also observed in commodity markets or in the record industry, especially in phonographic market. The probability density function for logarithm of weekly record sales changes is highly leptokurtic and characterized by a narrower and larger maximum, and by a fatter tail than in the Gaussian case.

What is the difference between fat tail and lean tail distributions?

Fat-tailed distributions are said to decay more slowly, allowing more room for outlier data to exist sometimes 4 or 5 standard deviations above the mean. As a result, extreme events are more likely to occur. Lean tail curves, on the other hand, have distributions that decrease exponentially from the mean.

Why do fatter tails matter in financial markets?

Even before the financial crisis, periods of financial stress had resulted in market conditions represented by fatter tails. This is important because normal distributions understate asset prices, stock returns and subsequent risk management strategies.

Which of the following is an example of fat tail distribution?

Cauchy distributions are examples of fat-tailed distributions. That is, if the complementary cumulative distribution of a random variable X can be expressed as then the distribution is said to have a fat tail if