Deutsche Bank - Analyst/Associate - Risk Methodology

Description of field of activity

Risk Methodology forms a core team within Risk Management to develop models that quantify market and credit risks. It is further divided into 3 broad sub-teams. Risk Management is responsible for managing fair value risk within Deutsche Bank. Risk management carries out this responsibility in an independent and neutral way, providing a comprehensive and independent view of market risks to Senior Management.

- The Market Risk Methodology (sub-) team is responsible for the development of DB's capital models for market risk, including the Value-at-Risk (VaR), Stressed Value-at-Risk, Comprehensive Risk Measure and Economic Capital methodology. Those risk metrics are used both for internal purposes to estimate and control market risk as well as for external purpose since they feed measures such as Risk Weighted Assets - Pillar 1 and Risk Bearing Capacity - Pillar 2.

- The Counterparty Credit Risk Methodology (sub-) team has responsibility for credit and counterparty risk methodology. In particular, the team develops, calibrates and maintains regulatory compliant, real-world simulation models across all asset classes. The resulting simulated forward-thinking exposures for Derivatives and Securities Financing Transactions are a key input to Regulatory (RWA) and Economic capital calculations for Counterparty and Credit risk 

- Credit Risk Methodology is involved with retail/CIB credit PD scorecards. Knowledge of SAS, regression/logistic regression, forecasting models or a good education/experience with statistics is highly desirable. Experience with retail credit products and banking book products like loans, insurance etc. would be desirable. Key stakeholders within the Risk Department are therefore the risk management functions Market Risk Management, Credit Risk Management (including Business Aligned Risk Managers), Operational Risk Management and Enterprise Risk Management.

Skills and Qualifications:

- Masters/PhD in Finance, Engineering, Economics, Statistics or other numerate discipline with excellent project experience and grades in quantitative and numerical coursework

- Excellent quantitative and analytical abilities as demonstrated by grades in mathematics / physics / statistics / engineering mathematics,

- Knowledge of financial pricing models, risk models would be desirable

- Good high level programming skills for ex. Matlab and experience in numerical coding

Responsibilities may include either of:

1. Market Risk Methodology

- Creating, implementing and documenting quantitative risk models and enhancements

- Providing quantitative and qualitative justifications for modelling choices, assumptions made, data selection, reliability of model inputs such market data

- Validating model choices by theoretical proof and support them with empirical evidence (e.g. statistical analysis of historical market data or back-testing)

- Recalibration of model parameters which are used in the internal risk and capital models for market risk, in particular for Value-at-Risk models

- Recalibration of model parameters which are used in the internal ratings based models for credit risk

- Recalibration of the period of significant financial stress for calculating SVaR

- Recalibration of scaling factors for estimation of materiality of risks-not in the VaR model

- Theoretical backtesting for the performance measurement of internal models, in particular Value-at-Risk models

- Calibrating parameters used in the Monte Carlo VaR system using MATLAB/R code and historical data

- Lift the design of the market data infrastructure to a strategic platform which supports back-testing for Historical Simulation and Monte-Carlo while responsible for dealing with very critical issues and escalations

2. Credit Risk Methodology

- Working with scorecards, SAS, regression (logistic), forecasting models for retail credit, Experience working with credit cards, loans, banking book credit

- Work with CRM units to explain outcomes of the score monitoring and/or data quality / operation teams on artefacts detected in the model development data

- Regular monitoring of the credit risk score quality for credit applications

- Data analysis and preparation for credit risk rating model development and parameter calibrations

- Analysis support for Risk Methodology teams in Europe, e.g. explaining outcomes of internal models for businesses