Investment Risk and Return Analysis In Python

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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 7 of 14

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Statistical Foundations of Return Distributions

Now that we've calculated and visualized returns, it's time to understand the statistical properties that describe them. Financial returns follow a probability distribution, which describes the likelihood of different return outcomes. The most common way to model financial returns is with a normal distribution, which is a basic, bell-shaped curve. But sometimes, this curve doesn't fit all the data we see, especially when returns are not evenly spread out. In those cases, we might use other types like the Student's t-distribution or the lognormal distribution.