PruVal Revisited
The Implementing Technical Standard for COREP (03/2016) and possible consequences for existing Prudent Valuation implementations.

In short

  • Implementation and application will happen in 2018.
  • Requirements for granularity and input-data, interims- and final results are fundamental for the choice of the suitable architecture.
  • Direct dependence of Prudent Valuation implementation and reporting-solution.
  • Adjustment need of existing architecture highly probable.


The draft of the adjusted Implementing Technical Standard (ITS) for the Supervisory Reporting was published in March 2016. It defines the details of the necessary reports of the Additional Valuation Adjustments (AVAs) according to Prudent Valuation Regulatory Technical Standard (RTS). The first application is planed for the end of 2018. The resulting implementations and adjustments of the existing Prudent Valuation-solution fall under a strict timeframe and the insecurity of a missing final publication of the standards. Moreover, the requirements of the ITS exceed those of the RTS significantly. The changes affect the following areas:

  • Reporting templates include information about exposures that haven’t been relevant to the Prudent Valuation implementation before.
  • The granularity of reporting templates exceeds the requirements of the RTS significantly. This refers to input data, interims and end results.
  • With the Upper Uncertainty a new parameter is being introduced that is not included in the RTS for Prudent Valuation.
  • At some points, the definition of technical requirements needs a suitable interpretation of the ITS.

In addition to the above points, the changed regulatory environment (e.g. IFRS 9 and the resulting Fair Value duty for certain exposures) requires a review and an adjustment of the Prudent Valuation implementation.

A decisive step towards the implementation of the new requirements is the suitable choice or adjustment of the system-architecture in combination with the existing Prudent Valuation-solution.

Figure 1: Example for a possible goal architecture (schematic)


The introduction of a normalisation layer and an information layer can be essential additions to the existing architecture.

Normalisation Layer: Merging Exposures.

A central normalisation layer makes sure that all requirements for the scope of exposure of the reporting can be shown. The following characteristics of this system component are relevant:

  • Information about all exposure that is relevant to the reporting is being aggregated at a central location.
  • The consistent data structure enables a compliant evaluation of all exposure.
  • It follows a central application of the prudent filter to all exposure.
  • It follows the persistence of all necessary data requirements in the information layer.
Hence, the normalisation layer ensures that:
  • the detailed reporting for the threshold reporting can be shown.
  • information about the exposure in the prudential filter is available and evaluable.
  • reporting requirements for the filter parameters can be aggregated for all exposure.

Risk Engine: Upper Uncertainty and Granularity.

The new requirements for the risk engine (risk engines) can be divided into Upper Uncertainty und the increased requirements for the granularity of the interims- and end-results.
After a definition of the Upper Uncerntainty (ITS only defines the concept as an evaluation adjustment of a 10%-quantile as opposed to a 90%-quantile in the calculation of the AVAs, but doesn’t specify a strict method) follows an extension of the calculation method. Effort and increased requirements depend amongst other things on the chosen model and the respective configuration. Depending on choice of model and possible symmetry assumptions you can deduce results with little effort from existing calculations.
The ITS demands information of a more granular level than institute or group level. For example, data has to be presented on portfolio level or per product type.
In general, branks already allocate AVAs on department, portfolio, or asset category level. But ordinarily this needs a calculation of AVAs on group or institute level. This guarantees the consideration of all netting and hedging effects. After that, the overall AVA, e.g. ranked by sensitivities or suitable parameters, is being allocated.
Higher granularity in the report directly refers to the calculation on portfolio or product type level. In total, this leads to higher AVAs and results in increased data requirements as opposed to the existing AVA-calculation.

This is an example for the higher requirements for the availability of interims- and end-results.

Information Layer: Granular and complete data.

The information layer is the basis for the generation of the report and contains the necessary data basis. All needed data are being directly imported from the normalisation layer or from the risk engine.
A consistent data pool and the connection to risk engines and normalisation layer allow for a high degree of automation of the process.
The information layer guarantees that:
  • even data that is not required in the risk engine is made available for the generation of the reporting templates.
  • there is a suitable data structure for the generation of the report and all necessary data is available.
  • a bundled and efficient generation of the report is possible via a central interface.
  • next to meeting the reporting requirements the collected data can be used for strategic controlling and a detailed control basis for management decisions for individual asset classes is possible.


The increased requirements for the Prudent Valuation implementation in comparison to RTS trigger an urgent need for action. This depends on the existing solution. A gap analysis shows the implementation needs and is the first step on the road to a suitable goal architecture. At some points, the guidelines of the ITS require an interpretation of the regulatory text. In order to meet the regulatory requirements in time (by the end of 2018), the implementation should start soon.
Please don’t hesitate to contact us for an in-depth discussion.
Andreas Hock
The Author
Andreas Hock

Since July 2015 Andreas Hock has been a manager at LPA in the Risk & Quant Consulting team. Before that, he studied mathematical finance at the Technical University of Munich and gained his first consulting experience at PwC. Here at LPA, he now deals with quantitative topics relating to the valuation of financial instruments, regulatory requirements and digitalisation.

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Andreas Hock
Andreas Hock