Importance of Stress Testing
Since the financial crisis of 2008, banks have been under additional pressure to ensure that they are compliant with the regulations required for stress tests. The idea behind stress testing is to ensure that the bank has enough cash in reserve in the case of a catastrophic market failure. Specifically, banks are mandated to move to a VaR (Value at Risk) model to an FRTB (Fundamental Review of the Trading Book) model. This FRTB model is much more stringent and forces a review on the desk level of the trading positions.
Detecting Market State
The type of tool used for hedging risk is dependent primarily on the state of the market. The client we were working with wanted to use reinforcement learning – the same type of learning that was used in game engines such as Alpha Go. They wanted an algorithm developed that could detect the “state” of the market and prompt traders to act accordingly based on historical data.
The introduction of these new rules meant that the client needed to have a system where they could run the FRTB tests required by the government – due to the complexity of such a system – it was important to not just understand the underlying mathematics behind the models but to also ensure that the testing tools developed were compliant with the requirements.
Difficulty of Implementation
Prior to DataSpartan taking over the work, the client had previously used another well known consultancy to undertake the project. Unfortunately, there was limited success with the platform built because they had used templates that were designed in a one size fits all and there were nuances and intricacies in the client systems that meant that a custom build was needed.
DataSpartan provided a custom build that was tailored to the nuances of the client data. In addition, other inputs had to be taken into account such as the Volker metrics in compliance with the Dodd Frank act. Custom APIs needed to be built to allow the in house developers to build team specific functionality on top of the platform. Because cross-team data was required, a standard data capturing tool was built to ensure that teams provided the system with an input that was compliant and made data integration into the calculation engine much easier.
The client was satisfied with the work and is using the system internally for stress testing purposes. When it was finished, the system could properly report on tests on a daily and weekly basis as well as flag any activity that was unusual. The system was clearly documented and handed off to an offshore development team to assist with maintenance and feature implementation with DataSpartan still having oversight for technically difficult challenges.