Counterparty Credit Risk Management CVA Demo – Overview

Xavier Bellouard |
June 27, 2011

This series of blog posts explains about Quartet FS’s counterparty credit risk management CVA solution and how to get the most out of our online CVA demo. In this post, we explain the counterparty credit risk management and CVA concepts, and discuss the OLAP cube structure.

ActivePivot™ & Counterparty Credit Risk Management

ActivePivot™ can be used with unmatched performance for counterparty exposure analysis, greatly facilitating counterparty credit risk management. Our cutting-edge technology enables:

  • Instant calculation of Credit Value Adjustment and other counterparty exposure risk measures for entire portfolios
  • Real time updates of data and calculations as we update our OLAP cube as required by any new information such as new trades, evolving market data…
  • Slicing and dicing of all these measures across multiple trade attributes and at any aggregation level, including an ability to instantly drill down to a trade level CVA, a feature only our counterparty credit risk management technology has proven possible
  • Sensitivity analysis of CVA instantly calculated for hedging purposes
  • Scenario testing to check environment or counterparty risk change impact at any level of the CVA analysis

These benefits ensure that ActivePivot™ is the perfect tool for both front office activities and counterparty credit risk management.

CVA Demo Description

We refer here to the demo version 2, as released in January 2011. This demo has all the previously mentioned benefits.
One can indeed both analyze the data across all dimensions to understand where counterparty default risks are located. And, we have also incorporated options for changing a specific counterparty rating (hence risk).
Please note we could also envision to use ActivePivot™ for simulating new trades, calculating dynamically required hedges of credit protection or computing risk weighted assets for capital requirements.

General Cube Description

The demo built around CVA creates an OLAP cube based upon a portfolio of trades each with a unique counterparty and associated future exposure. For each trade, there are 20 time points, from 1 year to 20 years from the as of date, for which we have exposure data. For each of those time points, 1,000 simulations have been calculated in a separate Risk Engine with different market data to compute the possible exposure for that trade at that point in time given the forecasted market data. These trades with their attribute and reference data, and simulations are fed into the ActivePivot™ cube as facts.

Counterparties are grouped into broader entities (ex : all Cathay Pacific legal entities) and we are able to compute on the fly the following measure from trade level to global bank level:

  • Exposure
  • Marginal Exposure (impact on Exposure when the considered trade or aggregate of trades is removed)
  • CVA
  • Marginal CVA (impact on CVA when the considered trade or aggregate of trades, is removed)
  • Delta CVA (sensitivity over CDS change)

The following screenshot shows for example the exposure in 5 years of all the trades fixed income trades with Cathay Pacific Airways Ltd. These trades are netted together.


In the cube, there is therefore one fact for each time point of each trade (which amounts to 20 facts per trade), each of these facts containing an array of 1,000 elements corresponding to the exposure simulations for this trade at this point in time.

In the next post we will explain about trade exposure in CVA, using the Exposure and Potential Future Exposure (PFE) measures.

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About the author

Picture of Xavier Bellouard

Xavier Bellouard

Co-founder & Managing Director
Managing Director with 30+ years experience and dual expertise in financial markets and technology. Result driven, with attention to details, a co-founder of Quotient/Summit, one of the most successful financial software products in Capital Markets, with a wide range of skills, including software design and development, professional services, sales, marketing, business development and people management. Co-founder of ActiveViam a data analytics platform specialized for Financial Services.

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