8843 Quantitative Analyst

Advantage Resourcing Uk Limited
1000.00 - 750000.00 GBP Daily
11 Sep 2017
19 Sep 2017
Contract Type
Department Overview
The quantitative modelling team with overall responsibility for market, liquidity and counterparty credit risk methods. The team sits within Enterprise Risk Architecture (ERA), which is part of the Risk Function of the group. The Risk Function is globally accountable for the definition of official risk policies, guidelines and procedures, as well as the quantification and monitoring of risks taken by the various business lines, to ensure alignment with risk appetite and policies.

The mission is to develop and continually improve the group's risk modelling & measurement, analysis and back-testing capabilities. Four streams, each responsible for a given asset class (IRFX, Credit / Repo, Equity / Commodity) or transversal aspects of risk methods (Cross-Product), supported by two architects responsible for ensuring consistency across methodological research and development activities.

The team's remit includes all the IMM models in use within the Bank, such as VaR / ES, Stressed VaR, IRC and CRM models in the market risk space, as well as EEPE, Stressed EEPE, Regulatory CVA models in the counterparty risk space.

Job Summary & Responsibilities
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.

Accordingly, the role does require a solid quantitative background in market risk.

Working in close partnership with quantitative analysts, risk analysts and Front Office research teams, the successful candidate will be expected to:

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.
In the short-term, the successful candidate will be expected to explore and fine-tune potential improvements to the Credit VaR model or to contribute to the DRC (Default risk charge) implementation. Eventually, work could also include non-FRTB related tasks such as investigating proposed improvements to the models or proving the immateriality of existing approximations in the model.

Our requirements

To be successful in this role, the candidate should meet the following requirements:

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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