This project focuses on analyzing financial data to calculate and visualize illiquidity measures, volatility, and returns over time for different PERMNOs (unique identifiers for stocks). It involves data collection, preprocessing, analysis, and visualization using pandas, NumPy, matplotlib, and statsmodels.

• Data Collection Summary from CRSP.
• Calculation of Illiquidity Measures.
• Visualization of Volatility and Returns.
• Analysis of Market and Illiquidity Portfolios.
• Innovations in Illiquidity and Liquidity Risk Assessment.

• Python 3.x
• pandas
• numpy
• matplotlib
• statsmodels

1. Ensure Python 3.x is installed on your system.
2. Clone this repository to your local machine.
3. Install the required Python packages:

After installing the required dependencies and setting up your dataset, you can run the script to perform the analysis. The script will output:

• A summary of the collected data.

• Visualizations for volatility and returns per PERMNO.

• Calculations of dollar volume and daily illiquidity measures.

• Analysis and visualization of illiquidity-weighted portfolios and market and illiquidity portfolios.

• Predictions and residuals for value-weighted returns and illiquidity measures using manual AR(2) predictions.