This repository designs a systemic approach of estimating the Value at Risk (VaR) and Expected Shortfall (ES) for a commodity portfolio.
-- The code development phase is as follows:
This section outlines how the group intends to develop the code to solve the problem above and achieve the set goal and objectives.
• Step 1: Data gathering and scraping using Python and APIs such as FRED and Yahoo Finance.
• Step 2: Write Python scripts to automate the data cleaning process whilst documenting steps taken to address every instance of bad data.
• Step 3: Exploratory data analysis using Pandas, Matplotlib and Seaborn.
• Step 4: Harmonize data as needed.
• Step 5: Build a sample portfolio and split data using a rolling window approach.
• Step 6: Write Python scripts that evaluate the portfolio’s risk using the three approaches and calculate the expected shortfall.
• Step 7: Compare and tabulate the measures using the historical and lookback periods using Pandas.