Glabex best practises of model development processes

1. Goal: The growing number and complexity of financial transactions have dramatically increased the importance of financial models models. Along with growing transitions, risk arise, which should be properly calculated, escalated and taken into consideration for future financial operations. So it is getting critical to get a better handle on model risk. Proper risk management and model building becomes best practices of any financial institution. Whether such models are pre-developed and maintained in-house, or managed externally by third-party vendors, it is essential that industry standard practises are employed in order to effectively manage model risk.

2. Understand Model Alternatives: It is fundamentally important to evaluate and properly calculate the variety of possible combinations of applicable outcomes of specific behaviour (market, credit, operations etc). Having said this, it is important to have set up a number of scenario outcomes, alternative models and compare outcomes form these. Such alternative could be derived from historical data, artificial data such as generated form Monte-Carlo simulation or or antithetical data. Examples can include parametric or non-parametric families of statistical distributions or aggregated distributions. In case if the portfolio contains complex derivatives, there are many different pricing models could be selected.
Determines Model Risks: Risks recognition and handling model uncertainty are crucial. In the current landscape there are now multiple types of model risk to assess. There are market Risk, Credit Risk, Operational Risk, Country risk (or Counterparty Risk), Liquidity Risk, Interest Rate risk and some others. Having determined the risk nature, based on real-world risk-measures such as VaR require the ability to model portfolio return distributions - in a way that is compatible with risk-neutral XVA value adjustments on MTM book pricing.




3. Attention to the Testing Outputs: The models should be reconciled and undergone back testing processes.
Model validation. Any issues that are identified will lead to further tests.

4. Independent Model Validation: model validation is separate practises and should be handled by independent individual .This role is responsible for reviewing the tests created by the quant developers, for creating additional tests to their own satisfaction, and for making the final assertion that test coverage and depth is sufficient.

5. Engage in Ongoing Monitoring and Learning: To minimize model risk, it is important to monitor the model’s performance over time. Incorporating feedback from live usage of pricing models is of high value in detecting problems early on, and should be accompanied by procedures for triggering investigations and resolutions of issues, and incorporating new tests as they become apparent. Risk models should also be continually back-tested using historical data, where available or antithetical and artificial data, including procedures to back-fill and maintain historical data when incorporating new instruments into the portfolio, in order to ensure a high-level of confidence in the model risk assessment.