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Core Steps for Validating XVA Models

Article Quant Q&A · Author: Ramsey
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Summary

The document asks how to validate complex XVA calculations, including credit and funding valuation adjustments, which involve simulation, valuation, and aggregation. Its answer outlines a broad validation process covering the model’s theoretical design, assumptions, and limitations; input data, parameter quality, and calibration; and whether the implementation matches the stated theory.

Validation also includes checking outputs under current market conditions and testing performance in stress scenarios to assess whether results remain reliable in extreme conditions. Governance, documentation, and controls over inputs and outputs are included as related parts of the process. The post points readers to external references but does not describe specific tests, acceptance criteria, or quantitative examples, so the checklist is a starting framework rather than a complete validation procedure.

Key ideas

  • Review the model theory, assumptions, and limitations.
  • Assess the quality and sourcing of market data and parameters, along with calibration.
  • Check that the system implementation follows the model specification.
  • Review outputs in current conditions and under stress scenarios.
  • Include governance, documentation, and input and output controls in the validation process.

Tags

Derivatives pricingRisk managementStatistics
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# Validation of XVA models


# Validation of XVA models












Hey what is the validation of XVA models (CVA, FVA etc)? As we know XVA calculation is rather complex problem (simulation, Valuation, aggregation) so what steps should be taken to check if the model can be used? Could someone briefly describe such a model validation process?

## Answer by Dimitri Vulis (score 2, accepted)

https://quant.stackexchange.com/a/68302

This recent paper https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3891120 (highly recommended in its entirety) has its entire section 5 devoted to XVA model validation.

You may also find these ORE slides insightful https://www.opensourcerisk.org/wp-content/uploads/2018/12/ore_user_meeting_2018_patrick_buechel.pdf

## Answer by B_B (score 1)

https://quant.stackexchange.com/a/68304

In general, the model validation consists of several steps:

- Checking the model design, i.e. model theory, model assumptions, model limitations, etc.;

- Checking the model inputs, i.e. market data sources, market data quality, model parameters quality, calibration process, etc.;

- Checking the model implementation, i.e. checking if the model is implemented in the system in line with the theory;

- Checking the model outputs, i.e. if the model produces correct numbers under current market conditions;

- Checking the model performance under stress scenarios, i.e. checking if the model produces reliable outcome under extreme market conditions;

- Checking all other related stuff, like model governance, model documentation, input controls, output controls, etc.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.