What is 'Leptokurtic'
Leptokurtic distributions are statistical distributions where there are extreme points (or outliers) along the X axis, resulting in a higher kurtosis than found in a normal distribution. These extreme values (outliers) are also evidence of “fat tails” relative to the normal distribution’s tail. A distribution is leptokurtic when the kurtosis value is a large positive number.
It is difficult to compare tail behavior via density plots because, even when the tails are thicker than the normal distribution, the values are close to zero as shown in the left panel graph below. On the other hand, tail thickness relative to the normal distribution is easily seen in a normal quantilequantile plot; see the right panel below
BREAKING DOWN 'Leptokurtic'
The prefix "lepto" means thin or skinny. The “thin and skinny” appearance of leptokurtic distributions is actually a consequence of the outliers, which stretches the X axis of the plot, making the bulk of the data appear to occupy a narrow vertical strip. "Kurtosis" means “arched” or “bulging” based on its Greek origins, but these terms do not characterize distributions correctly. Rather, leptokurtic distributions have heavy tails, or outliers, when compared to mesokurtic or platykurtic distributions.
When analyzing historical returns, kurtosis helps the investor gauge an asset's level of risk. A leptokurtic distribution means that the investor will experience occasional large fluctuations (e.g. five or more standard deviations from the mean) more often than predicted by the normal distribution.
Leptokurtosis and Estimated Value at Risk
Leptokurtosis can impact how analysts estimate value at risk (VaR). An investor using a normal distribution to estimate VaR may overestimate at low levels of significance, but might underestimate at high levels of significance if the return distribution is leptokurtic. This is the result of the leptokurtic distribution having a fatter tail, where returns far from the mean (e.g., five or more standard deviations) are more likely than VaR calculations based on the normal distribution would predict. The fat tail means that risk is coming from the extreme observations that occur occasionally. Therefore, conservative investors should avoid this type of return distribution.
Leptokurtosis and Normal Distribution
To determine the classification of kurtosis, a normal distribution is used as a comparison point. The usual kurtosis coefficient is the average of the standardized data values (z values), each taken to the fourth power. For a normal distribution, this quantity is an estimate of the number 3.0, so often leptokurtic distributions are characterized by a kurtosis coefficient that is greater than 3.0. Equivalently, the term “excess kurtosis” refers to the kurtosis coefficient minus 3.0, so that leptokurtic distributions are also characterized by excess kurtosis greater than 0.0.
Distributions with higher kurtosis are more heavytailed; i.e., more prone to serious outliers.
Leptokurtosis, Mesokurtosis and Platykurtosis
While leptokurtosis refers to greater outlier potential, mesokurtosis and playkurtosis describe lesser outlier potential. Mesokurtic distributions have kurtosis near 3.0, meaning that their outlier character is similar to that of the normal distribution. Platykurtic distributions have kurtosis less than 3.0, thus exhibit less kurtosis than a normal distribution, implying that their outlier characteristic is less extreme than that of the normal distribution.

Kurtosis
Kurtosis is a statistical measure used to describe the distribution ... 
Platykurtic
Platykurtic is a particular type of statistical distribution ... 
Tail Risk
Tail risk is portfolio risk that arises when the possibility ... 
Probability Distribution
A probability distribution is a statistical function that describes ... 
Distribution Management
Distribution management refers to overseeing the movement of ... 
Uniform Distribution
In statistics, a type of probability distribution in which all ...

Investing
Most Common Probability Distributions
In this article, we'll go over a few of the most popular probability distributions and show you how to calculate them. 
Investing
Stock Market Risk: Wagging The Tails
The bell curve is an excellent way to evaluate stock market risk over the long term. 
Investing
Calculating volatility: A simplified approach
Though most investors use standard deviation to determine volatility, there's an easier and more accurate way of doing it: the historical method. 
Investing
Fat Tail Risk Makes Global Warming Scarier
The cost of global warming does not take into account climate changerelated catastrophes. Here's where fattail distributions come in. 
Personal Finance
Backtesting ValueatRisk (VaR): The Basics
Learn how to test your VaR model for accuracy. 
Trading
The Normal Distribution Table, Explained
The normal distribution formula is based on two simple parameters  mean and standard deviation 
Retirement
Best ways to use your 401(k) without a penalty
Learn how to avoid incurring additional tax penalties when using your 401(k) before retirement with hardship distributions and loans offered by your plan. 
Investing
Bet Smarter With the Monte Carlo Simulation
This technique can reduce uncertainty in estimating future outcomes. 
Investing
Value at Risk (VaR)
Value at risk, often referred to as VaR, measures the amount of potential loss that could happen in an investment or a portfolio of investments over a given time period.