Risk model management must meet stringent regulatory provisions
CCP risk model management determines the margin requirements and strikes the balance between market competitiveness and model robustness. These developments have come to regulatory attention¹ as sound modeling approaches are essential for ensuring the long term viability of CCPs. Clearing houses must now conduct periodic reviews of each model to determine whether it achieves target coverage with a low level of pro-cyclicality. As of now, regulatory compliance can only be achieved through active management of model risk which is most effectively done by independent model developers such as swissQuant Group.
Regulatory approved Intelligent Technology
swissQuant Group is the world leader in CCP model development and validation. We have the requisite experience, knowledge, skills, and a high level of technical expertise to validate complex models, both in structure and application. Our Intelligent Technology encompasses highly flexible modules for data clearing, proxy generation and validation, and data interfaces. It also encompasses sophisticated database designs and data models able to process large data sets with low retrieval time.
Ask the experts in derivatives risk management and pricing
Model risk management examines the processes and activities intended to verify that models are performing in line with their design objectives and business applications. It also identifies potential limitations and assumptions and assesses their possible impact on overall risk profiles. For this reason, it is best performed by confirmed experts with appropriate experience and influence.
Pre-emptive Model Risk Mitigation
With CheQ5, swissQuant Group has developed a structured auditing process consisting of five interrelated checkpoints to improve internal model design and development. Years of risk and pricing model development and validation have resulted in extensive model validation libraries able to validate model assumptions even for large panel data. Our modular libraries for integrated model consistency testing, high-dimensional statistical tests, and visual inspection of high-dimensional data are readily available allowing for extremely fast implementation and a considerable reduction of validation project time.
¹ European Market Infrastructure Regulations (EMIR), Dodd-Frank Wall Street Reform and Consumer Protection Act
Proven Intelligent Technology
Construction of risk and pricing models
Development and implementation of hedging frameworks
Implementation of large-scale risks systems (Big Data)
Regulatory approved model validation and auditing frameworks
Risk model management to the point
Data and Implementation Quality
The first check involves investigating input data for differences to alternative data sources and evaluating proxies where data is missing. Model implementation is reviewed; good theory must be accurately translated into self-explaining code.
Conceptual Soundness and Assumptions
In a second check, model assumptions are explicitly and implicitly reviewed by reverting to our extensive software library of statistical analyses and stylized facts. Possible complexity reductions are reviewed and applied methodologies are tested and compared to their empirical model behavior.
Back Testing and Stress Testing
Model robustness and accuracy are exposed to various extreme but plausible scenarios. Changes in inputs and parameters on model outputs are gauged to determine expected ranges. Correspondingtail risks are highlighted.
Model in Model Consistency
This check validates the consistency of model assumptions throughout all model stages to highlight risk exposures. The interplay of all submodels is back tested and stress tested in historical, as well as artificial but plausible environments. Model interdependencies and convergence tests are investigated to ensure econometric test reliability.
The final check evaluates the predictive power of different models and their performance. Observed deviations are assessed and corrective actions suggested. Our automatized frameworks allow for fast representation of the final results.
ReferencesswissQuant Group has concluded many successful validations and implementations of pricing and large-scale risk systems. We are one of the largest independent Quant teams in Europe dedicated to delivering lasting client value as demonstrated by the following selected reference projects:
Improved margining system of a top-tier European stock exchange clearing house through superior methodology and full model validation.
FX option model development for the largest U.S. clearing house.
Design of large-scale portfolio risk systems with hundreds of thousands of portfolios of all asset classes for the world’s leading wealth managers.
Validation of applied valuation and risk models and methods of new structured products for a provider of fair value pricing.