Risk Engine (RE) is a system that provides market risk measurement and analysis of investments. It is applicable in the financial and insurance institutions, treasury, investment fonds, hedge fonds and all companies with an advanced investment / cash management.

Risk Engine´s main features include:

  • Precise modelling of the market – prices, indices, yields, spreads, scenarios, statistics, yield and credit spread curve bootstrapping, normalization and interpolation, etc.
  • Precise modelling of investments – cashflow generation, conventions, optionality, etc.
  • Advanced stress testing on all types of factors in groups or per factor basis
  • Multiple models used to represent the positions:
    • Internal model – discounted cash flow, rules, data driven or predictive (AI). Its main goal is to represent the positions as observed/defined on the tradable market
    • Delta model – by definition or derived from internal model. Its main goal is to represent the position with respect to the regulatory requirements e.g. – SST
    • Delta-Gamma model - by definition or derived from internal model. Its main goal is to represent the position with respect to the regulatory requirements e.g. – SST
    • User defined by rules. Perfect for modelling and analysis of new contracts in fonds, insurance companies, banks, credit institutions. In contrast with the usual excel modelling, RE models can undergo risk analysis, stress testing, behavior analysis available in the system
    • Data driven and predictive models – based on the known market history, they reverse engineer the position relationship to the marker. The main goal is to represent position with known behavior but unknown expected behavior – e.g. Private equity, Rights, etc.
  • Advanced risk measurement methods based on Monte-Carlo simulation taking into account all details of the contracts (see Fig. 1. below)
  • Designed for integration with other applications, but also operates standalone
  • High performance:
    • Ad-hock concurrent calculations
    • Usual simulation of around 1000 positions. Some test cases include 100.000 positions
    • Horizontal scaling of evaluations
  • Access to the system ONLY via the respective API or GUI which makes the integration easy
    • Supported APIs – Messaging (JMS), REST, WS-SOAP
    • Browser or Tablet compatible GUI. Uses HTML/CSS/JavaScript only – no applets, flash and alike.

Swiss Solvency Test (SST) in Risk Engine

Risk Engine (RE) provides more advanced calculations and model than the ones mandated by Swiss Solvency Test (SST), so the implementation / calculation of SST is a matter of configuration.

The necessary configuration includes:

  • Configuration of the market factors which take part in the SST market universe
  • Import of the SST market universe quotes (see Fig. 2. below)
  • Configuration of the RE market scenarios to describe the Finma sceneario cases – Quadranten, Pandemie, etc.
  • Configuration of the RE factor dimension reduction to map the SST market factor universe to the actual portfolio / system factors:
    • The mapping may be derived from market data, user defined (fixed) or both
    • If data driven model is used, the instruments may be configured to track the Finma market universe and dimension reduction configuration is not needed
  • Calculation of the portfolio with the SST parameters – Smith-Willson, confidence, correlation matrix correction, factor deflections, etc.

Under the hood, the system builds the Delta / Delta-Gamma models by applying the necessary deflections. Then, the required calculations – stress tests, Monte-Carlo simulation, etc. are applied. Along with SST models, the more precise internal model may undergo the same calculation so that the results of the different models may be compared.

For all calculations and models, the results are calculated per position and per factor level first, and aggregated on portfolio / sub-portfolio level next. This process takes into account the portfolio structure (asset allocation). See Fig. 3. and Fig. 4. below.

Fig. 1. Distribution chart of the Monte Carlo Simulation in RE

Fig. 2. Market factor quotes for gold along the time axis and the time series chart for the factor.

Fig. 3. Delta / Gamma results on position level

Fig. 4. Delta / Gamma results. The results are on risk factor level.