A UK based food manufacturer has excess cash and has set up its own fund to invest into equities and shares. For reporting purposes, this company wants to know for each position in their 15 portfolios, what is its credit exposure? By knowing the price of the CDS 5Y, the food manufacturer can determine how much they have to pay to offset the credit risk associated to an instrument within a portfolio. When utilizing LSEG's Workspace Portfolio management tools, users have access to a powerful suite of details. Unfortunately, the credit exposure information is neither available directly within the portfolio nor in any templates of the portfolio reporting tool.
Does that mean it’s not possible to provide this information when the CDS 5Y price is quoted by LSEG? Absolutely not! In this article, we will describe how to leverage Python to enrich the portfolio details with the credit risk exposures.
Refer to the Credit Risk Exposure Article defined within the LSEG Developer Community for more details.
The source code presented in this project has been written by LSEG for the purpose of illustrating the concepts of creating example scenarios using the LSEG Data Library for Python.
Note: To ask questions and benefit from the learning material, I recommend you to register on the LSEG Developer Community
To execute the notebook, you will require a desktop license for LSEG Workspace.
- Notebook can be directly loaded into LSEG Workspace CodeBook
- Yao Koffi Kouassi
- Nick Zincone