Investment Risk and Return Analysis In Python

14 lessons0%

Resources

Lessons

Investment Risk and Returns

Discreet Vs Logarithmic Returns – Which One to Use?

Analyzing Financial Time Series Data with Python

Step 1: Load the Data

Step 2: Calculate Returns

Step 3: Visualize the Distribution of Returns

Statistical Foundations of Return Distributions

Moments of a Distribution

Mean, Variance, and Normal Distribution

Calculating the First and Second Moments (Mu and Variance) in Python

Higher Moments of Distribution

Calculate Skewness and Kurtosis in Python

Conducting Normality Tests in Python: Practical Applications

Conclusion

Lesson 11 of 14

Premium lesson

Buy this course or get a membership to access all premium lessons.

One-time purchase - yours to keep forever

Access all premium courses and features with one membership

Already have access? Sign in here

AI Instructor

Hello, how can I help today?

Ask questions about this lesson and get instant answers.

Higher Moments of Distribution

After exploring the mean and variance of financial returns, which provide insights into the average performance and volatility of an investment, it's important to study the higher moments of distribution: skewness and kurtosis. Skewness and kurtosis provide insights into the asymmetry and fatness of a distribution. These moments offer a deeper understanding of the behavior of return distributions, particularly in terms of asymmetry and tail risk.

Premium membership required

Upgrade to premium to unlock all videos and courses