Advancement Internal Model

Initial Situation

A large German asset manager uses vendor software in combination with self-developed extensions based on Excel VBA to measure market risk figures (especially scenario values, P&L, VaR, etc.) in the Solvency II context. New business requirements and release cycles require regular upgrades of the system.

Project Scope

  • Preparation of pre-study for Dynamic Volatility Adjustment (DVA).
    • Calculation of yield curve adjustments for each market risk scenario.
    • Design of new yield curve stacking in the market risk system.
  • Implementation of Smith-Wilson extrapolation in the market risk system.
  • Optimisation of the efficiency and quality of the plausibility process by automating process steps.

Our Contribution

  • Developing a prototype for calculating the DVA using Smith-Wilson extrapolation.
  • Validating the results of the DVA prototype.
  • Further developing the overall architecture of market risk measurement.
    • Designing and implementing an R-based application to reflect the plausibility check of market data used in risk measurement.
    • Optimising existing tools used for plausibility checks of the market risk figures.

Customer Benefit

By improving the tools for plausibility checks of the market risk calculation, the need for manual interventions could be significantly reduced. The prototypical implementation of the DVA calculation and Smith-Wilson extrapolation proved the feasibility and allowed a reliable effort estimation for the productive implementation.

Relevant Skills/Tools

  • Transact/HANA SQL
  • R, in particular Shiny Apps
  • Simcorp Dimension
  • Azure DevOps
  • Excel VBA

Interesse an weiteren Projektreferenzen?

  • P&L Explain Enhancement

    Enhancement of P&L Explain to increase the efficiency of analyses of valuation and risk indicators and improve their informative value.

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  • Advancement Internal Model

    Pre-study for Dynamic Volatility Adjustment (DVA) using a Smith-Wilson extrapolation in market risk measurement.

    W E I T E R L E S E N