This document discusses skewness and kurtosis in a financial context. It defines skewness as a measure of asymmetry in a distribution, with positive skewness indicating a long right tail and negative skewness a long left tail. Kurtosis is defined as a measure of the "peakedness" of a probability distribution, with positive excess kurtosis indicating flatness/long fat tails and negative excess kurtosis indicating peakedness. Formulas are provided for calculating skewness and kurtosis from a data set. Examples of positively and negatively skewed distributions are given to illustrate these concepts.

1. NAVIN BAFNA ARVIND SHAH ABAHAN BANERJEE ABHISHEK CHANDRA ABHISHEK DHAWAN FINANCIAL MATHS GROUP PROJECT

2. “ Mathematics is the only science where one never knows what one is talking about nor whether what is said is true” - Bertrand Russell LET US GIVE A TRY !!!!!

3. SKEWNESS AND KURTOSIS

5. NORMAL DISTRIBUTION SKEWNESS NEGATIVE POSITIVE

6. CALCULATING SKEWNESS Given a set of returns r, t = 1,2…..T Where r and sˆ are the estimated average and standard deviation

9. Spreadsheet - for Positively Skewed Distribution…

12. KURTOSIS

13. CALCULATING KURTOSIS

14. Example: Kurtosis

16. THANK YOU !!! To Prof. Mahendra Mehta