Back-testing Expected Shortfall
New Model to handle Risks beyond VaRBack-testing of Expected Shortfall has received a lot of attention post financial crisis, with organizations realizing that just managing Risks using VaR may not be sufficient. However, lack of proper research and models on using Expected Shortfall as a more “definitive” Risk measure has been a consistent hurdle for companies in adopting Expected Shortfall.
This Whitepaper develops a new model for back-testing Expected Shortfall and shows how organizations can implement it. It also gives guidance on how this model can be put into practice by Risk management teams of Commodity Trading firms so they can manage extreme loss scenarios far better than ever before.
We have used 6 different commodities to test these models and all of them gave encouraging results. The paper ends with a conclusion that if 2 of the models are used in conjunction, they can not only offer a far more definitive protection to an organization from huge draw-downs, but also allow gradual build-up of Risk Capital instead of a huge, bullet payment into the Risk Capital account.
What you’ll learn in this Whitepaper:
- Bootstrap Back-testing results of VaR and Expected Shortfall
- 2 new Back-testing parameters, with models that reach deeper into the fat-tails of distribution
- Results from the 2 Back-testing parameters
- How to implement the Back-testing parameters and avoid Extreme Loss scenarios
- An ERR (Excess Risk Ratio) driven model to assess and manage tail-end loss scenarios