**Kurtosis** is a measure used in statistics to measure weight in the tails. A normal curve with a mean of zero has a kurtosis of 3. Kurtosis in simple terms measures outliers in a distribution of data sets. Kurtosis is a measure of the fourth moment to quantitatively measure the value of outliers which are data points at extreme ends of the curve. These points are beyond 3 standard deviations and any value of kurtosis above 3 suggests a non-normal bell curve. Kurtosis plays a fundamental role in measuring risk and a key measure in statistics and quantitative analysis. Outliers in financial markets can be defined as Black Swan events (based on the book **The Black Swan: The Impact of the Highly Improbable by Nassim Taleb**) which are highly improbable and its occurrence can cause financial havoc in markets. Russian default resulting in LTCM collapse is one such example which led to financial and equity turmoil. Kurtosis differs from skewness, which measures symmetry of the distribution of data points around the mean and its a measure of the 3rd moment. For a normal distribution, skewness is zero. Any distribution or set of data points with Kurtosis greater than 3 is called Leptokurtic resulting in heavier tails consisting of more outlier data points. Kurtosis concept is key to policy analysis since outliers ultimately define regression analysis through the best fit curve. We will look into this concept at a later date…

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