Noetzold & Noetzold

Requirements for Advanced Risk Management Systems

Risk Data Collection

Data collection quality is defined by the following parameters:

  • Fully quantitative data collection for risks, opportunities, correlations, measures, and risk drivers.
  • Consistent, overlap-free, and uniform risk database.
  • Consideration of different risk types (e.g. event risks and market risks).
  • Efficient and user-friendly data collection (e.g. with experienced risk profilers).
  • Consideration of risk parameters (distinguishing between gross and net risk values, e.g. growth rate, efficiency of measures, etc.).
  • Unambiguous assignment (e.g. to risk owners, Profit and Loss statements, corporate units).
  • Consideration of estimation errors.
  • Standardized risk quantification and evaluation.

Risk Management Software

Requirements for an advanced risk management software are:

  • Use of advanced risk management models and methods.
  • High-performance high-precision risk engine (i.e. performance/precision gain about 10^6 compared to most spreadsheet plugins).
  • Low implementation costs due to compatibility with main corporate standards.
  • Intranet solutions (i.e. easily integrable into any existing IT environment).
  • Highly efficient database structure.
  • User-friendly and intuitive interfaces.
  • Modular and extensible system architecture (e.g. easily customizable to specific customer needs).

Risk Management Models and Technology

Risk management models and technology are essential for the calculation of correct risk results. Conventional risk management systems without risk management models or without sophisticated aggregators (i.e. simulators) generally are not able to generate adequate results, since the main risk figures referring to extreme events, such as Value-at-Risk, Expected Loss, quantiles, etc., require advanced technology to be extracted with sensible precision. The minimum requirements concerning models and technology for calculating correct risk results are:

  • Aggregation of risks, opportunities, risk drivers with consideration of all mutual correlations.
  • Risk aggregation for any number of risks, opportunities, measures, and risk drivers.
  • Coherent and consistent risk model for different risk types, such as event and market risks.

Risk Results

The quality of the risk results depends on the quality of the risk data input quality and on the risk management models and technology used to process the risk data. Requirements for risk management output data are:

  • Risk-adjusted P&L statement and/or balance sheet.
  • Risk figures (e.g. VaR, CFaR, EaR, RaC, RAROC, RORAC, Expected Loss, quantiles, probability distributions).
  • Diversification effects and concentration effects (risk bundles).
  • Risk and risk driver concentrations, contributions, and sensitivities.
  • Risk ranking and risk map.
  • Risk-return matrix and optimization.
  • Optimal risk mitigation measures for individual risks and on corporate level.