The xVA Challenge; could open source be the answer?

By Stuart Smith, Co-Head Business Development Risk & Data

‍Overview

xVA, or “X-Value Adjustment,” refers to a group of adjustments made to the value of derivative contracts to account for various risks and costs associated with the trade. The “X” in xVA stands for “uncertain” or “unknown,” reflecting the fact that the adjustments are made to account for risks that are difficult to predict or measure.

The main types of xVA are:

• CVA (Credit Value Adjustment): This adjustment reflects the counterparty credit risk associated with a trade. It is the difference between the risk-free value of the trade and its value when the counterparty’s credit risk is taken into account.
• DVA (Debit Value Adjustment): This adjustment reflects the credit risk of the entity holding the trade (i.e. the bank). It is the difference between the risk-free value of the trade and its value when the bank’s credit risk is taken into account.
• FVA (Funding Value Adjustment): This adjustment reflects the cost of funding the trade. It accounts for the difference between the funding cost for the bank and the funding cost for the counterparty.
• KVA (Capital Value Adjustment): This adjustment reflects the cost of holding capital against the risk associated with the trade. It accounts for the difference between the cost of holding regulatory capital against the trade and the economic cost of the capital.
• MVA (Margin Value Adjustment): This adjustment reflects the cost of posting collateral or margin for the trade. It accounts for the difference between the cost of the collateral for the bank and the collateral for the counterparty.

Together, these adjustments make up xVA, which is used by financial institutions to accurately price and manage derivative trades. The exact calculation of xVA can be complex, and depends on a number of factors, including the type of derivative, the counterparty, and market conditions.

‍Initial Margin ‘Changes the Game’

When calculating xVA it has always been important to consider the impacts of collateral, however the introduction of Initial Margin has both changed the importance of collateral and increased the complexity in its calculation.

The way in which Initial Margin has been introduced means that rather than a binary switch over from one regime to the other, we have seen a gradual ramp up of the initial margin being posted. This is due both to the phased implementation of ISDA SIMM^TM and the fact that only new trading is subject to the new IM rules and therefore individual portfolios are also subject to a relatively gradual ramp up.

The current position is that bilateral relationships between firms in Phases 1-4 have reached maturity in terms of the IM they are posting, where the natural variance in posting amounts is larger than any remaining increase due to turnover of legacy trades. In contrast, firms in Phases 5-6 are still seeing very rapid increases of their IM exposure with all counterparties. As they are still seeing rapid turnover of their legacy trades with new trading subject to IM.

The implication for xVA is that for major trading relationships its vital that IM is accounted for, both for the decrease in CVA that it provides and in MVA for the increase in funding cost. Modelling either affect can be highly challenging, as the Initial Margin calculation itself is a risk-based calculation, which will vary on any future path of the simulation. Several approaches have been proposed to account for this, and risk engines must be upgraded to reflect the new reality.

‍Benefits (and limitations) of Open-source Software

There are several benefits of using open-source software for the calculation of xVA:

• Cost: Open-source software is usually free to use, which can save financial institutions a significant amount of money compared to using proprietary software.
• Flexibility: Open-source software is highly customizable and can be adapted to the specific needs of an organization. This can be particularly useful for financial institutions that have unique or complex requirements for calculating xVA.
• Transparency: Open-source software is developed in a collaborative and transparent way, which means that the source code is available for anyone to view and audit. This can increase trust and confidence in the accuracy and reliability of the software.

Overall, the use of open-source software for the calculation of xVA can provide financial institutions with a cost effective, flexible, transparent, and innovative solution for managing their derivative trades.

Quant.lib is one of the examples of the most commonly used quantitative libraries - it has over 100 contributors including highly experienced risk developers from major banks. The models are under constant scrutiny and review. It also provides many aspiring quantitative developers with the opportunity to demonstrate their capabilities. This openness gives a much higher degree of scrutiny than many proprietary engines which typically have proprietary core code stacks.

While open-source software for the calculation of xVA has many benefits, there are also some limitations to consider:

• Lack of vendor support: Unlike proprietary software, open-source software may not have firms who are willing to offer support. As support is often a requirement for regulated firms, this can make it challenging to use open-source software.
• Integration challenges: Integrating open-source software into an existing IT infrastructure can be complex and time consuming, particularly if the organization has a heterogeneous environment with multiple legacy systems.
• Documentation: In an area where models are key, it is essential that those models can be validated. This can be a challenge if documentation is incomplete, or inconsistent.

For open-source to achieve its full potential in this area, these challenges must be overcome.

‍ORE an Open-source Risk Engine

The Open-source Risk Engine (ORE) project is an attempt to take the open-source quantitative library. This is particularly relevant in the field of xVA where flexibility to meeting daily challenges is highly prized by front office quantitative developers. The complete transparency of open-source means that developers can always unpick any numbers which are generated by the engine’s calculations, but also make changes where needed to meet new or changing requirements.

ORE has played a key role in helping smaller firms achieve compliance for ISDA SIMM^TM as the risk engine underlying the leading SaaS SIMM^TM solution. It has over 100 clients calculating and reconciling sensitivities daily to drive the exchange of initial margin. The use of ORE which drives detailed reconciliation of models has driven a high degree of quality and ensures that the level of documentation and support can meet high regulatory standards such as SR11-7 for model governance.

‍Conclusions

Open-source solutions have the potential to help smaller banks compete in the xVA space. Where banks have neither the budget to build a proprietary engine from the ground up or purchase industry leading commercial solutions, they can use open-source libraries or complete solutions to accelerate development and compete with much larger banks.The model allows them to maintain flexibility and agility but also have the models available to accurately price complex trades with a range of xVA adjustments.

‍About Stuart Smith

Stuart Smith joined Acadia in 2022 as Co-head of Business Development. In his role, Stuart is responsible for driving the strategy, development and growth across Acadia’s Risk and Data suite of solutions. Stuart has worked in the capital markets industry for over ten years, implementing a range of risk systems with financial institutions globally. Prior to joining Acadia, he led the development of FIS’ market and credit risk solutions working with clients on complex problems including regulatory compliance, real time credit limits and innovative high performance aggregation solutions. Stuart holds a Masters (MPhys) from Oxford University and PhD in Quantum Electronic Engineering.