09. Kurtosis in Finance Demo

Understanding Skewness and Kurtosis in Finance

This tutorial guides learners through calculating skewness and kurtosis using Python, essential metrics for analyzing the distribution of financial returns.

Concepts Explained:

• Skewness: Measures symmetry in data distribution.
  • Negative Skewness: More outliers on the left side.
  • Positive Skewness: More outliers on the right side.
  • Near Zero Skewness: Symmetrical distribution.
• Kurtosis: Assesses the data's tail heaviness (propensity for outliers).
  • Kurtosis > 3: Indicates fat tails, critical for understanding risks.
  • Excess Kurtosis: Pandas default, requires adding 3 to convert.

Process Overview:

• Utilize the Yahoo Finance API to retrieve data from the S&P 500.
• Calculate daily returns by analyzing percentage changes.
• Use Pandas to compute the skewness and kurtosis of returns:
  • daily_returns.skew() for skewness.
  • daily_returns.kurtosis() for excess kurtosis.

Importance:

Analyzing skewness and kurtosis offers insights into potential market extremes, vital for risk management and model development in finance.