|Salary:||£750 - £1000 per day|
The principle requirement of the role is to design and implement (or modify) market risk measurement methods in the context of the Fundamental Review of the Trading Book (FRTB). This will typically cover simulation models, calibration methods, etc. but will also require a general understanding of the wider FRTB requirements to ensure compliance and readiness with regard to as well as downstream processes - such as P&L attribution and back-testing models.
• Contribute to the delivery of methodology projects, gathering and documenting requirements, considering all stakeholders’ interest, regulatory constraints and any potential deficiencies in the current methods exposed by quality assurance and regulatory processes (e.g. back-testing, P&L attribution);
• Investigate, analyse and design risk methods, respecting the aims of accurately capturing risks whilst considering regulatory, system or other environmental constraints;
• Design, develop and test code changes required to implement the risk methods in the risk systems, whilst assisting the technical teams responsible for the production environment;
• Ensure the methods are adequately documented to support internal reviews and validation by internal auditors or regulators, by providing sufficient developmental evidences (i.e. materiality studies, description of assumptions, benchmarking against external methodologies and justification of methodological choices); take the lead in ensuring the successful review by model validation teams.
SKILLS AND EXPERIENCE
• A strong academic background, with at minima a Masters in mathematics, physics or quantitative finance;
• Proven experience in a quantitative finance environment, preferably in a market risk modelling capacity (knowledge of asset simulation and stochastic models is a must; whilst not mandatory strictly speaking, previous exposure to FRTB frameworks would be highly preferred);
• Exposure to risk modelling for credit instruments;
• Good grasp of the FRTB regulatory framework and its implications on the bank’s operations (Stressed ES calculations, NMRF, P&L attribution, etc.);
• Practical knowledge of derivatives, their risk drivers and pricing models;
• Design and implementation of quantitative models, using C# or C++ in a source-controlled environment;
• Ability to contribute and operate with minimum level of supervision.
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