XVA Analytics

XVA Analytics
by Thomas Schwiertz

XVA Analytics: risk metrics to measure business risks in derivative deals

The global financial crisis of 2007 did put known business rules in capital markets into question. An example was the perception of derivative deals. Had derivatives until then been seen as a sufficient insurance against business risks, the structural failure of major institutions such as Lehman Bros. fostered the need for a more fundamental risk assessment (and protection) within the finance industry.

XVA Analytics has emerged as the key response to this need. And Covid-19 just increased the needs for treasury and finance.

Our Risk Analytics is defined as a thorough assessment of the default risk of the respective business partners to determine a required “insurance amount” to give security to the business deal. The XVA value calculation is built upon four individual metrics, that are described below:

    • CVA (credit valuation adjustment) -> Difference between an entirely risk-free portfolio and and identical portfolio considering the default risk of the respective business partner. Essentially the value reduction of a deal from the perspective of business partner A caused by the default risk of business partner B. 
    • DVA (debt valuation adjustment) -> Value reduction of a business deal caused by a party’s own default risk. 
        • DVA and CVA equal each other, depending on perspective:
          The CVA of party A is the same as the DVA of party B and likewise.
    • FVA (funding valuation adjustment) -> Value reduction of a deal due to liquidity risks.
    • KVA (kapital valuation adjustment) -> Money amount that needs to be put aside due to regulatory rules as an “insurance” of market partners due to each party’s default risk. Equals CVA / DVA in amount (as CVA can be seen as the “market value” of the default risk.

In a nutshell XVA Analytics determines a value to which a derivative deal needs to be additionally “insured” to avoid the risk of a complete financial loss due to the default risk of a business partner.

Please see below for a schema on XVA analytics calculation:

Source: the above illustration was created by financial industry expert Jon Gregory and kindly granted to us for use on XVA-blockchain.com

Blockchain technology improves XVA Analytics in Collateral Management

Use UTI and LEI data for your collateral optimization

We are applying blockchain technology for master- and reference data to enhance your in-house XVA adjustments for trading and counterparty risk management. Achieve regulatory compliance with us. Thanks to superior master- and reference data reporting our use case beats or enhances competitive offerings strongly with regards to operational efficiency.

Whether seeking to meet regulatory requirements, hedging or actively trading your XVA positions, our XVA Analytics offering provides state of the art reporting for managing XVA. We identify impact on counterparty risk, collateral, and profitability across the institution.

Our XVA Analytics Oracle gives users the ability to calculate, analyze and limit exposures across business units and minimize capital charges for Basel III and SA-CCR compliance, and provides front office support with fast and accurate PFE, CVA/DVA, KVA, COLVA and FVA calculations, by using Adjoint Automatic Differentiation. We offer professional services to connect your in-house risk engine to our XVA blockchain or to extend your current pricing platform.

While building portfolio data on distributed ledger technology and thus providing superior features such as security, transparency and stability, our XVA Analytics offers privacy for business critical data of participating institutions e.g. your own positions. A blockchain approach combines transperancy (regulator, audit) and security alike.

XVA Analytics lives on your platform connected to the XVA-Blockchain DLT data, to achieve a next generation IT approach in margining, risk analysis, and trade management. Our offering combines wrong-way risk reporting and advanced collateral optimization by acting as your master- and reference data provider.

Bond- and collateral data help to bootstrap market data of the XVA Blockchain and can on top define your BCBS 239 sources. Our blockchain consensus for market data is based on official ECB interest rates and exchange rates to define haircuts.

The universe of eligible collateral can be huge and is a subject to tokenization itself. As a first step, electronic issuance should be primarily made possible through bearer bonds (Schuldverschreibungen) according to the latest legislations in Germany.

It will be important to see how German legislation interacts with EU initiatives concerning the application of financial regulation to security tokens in the future. Cryptoassets have been listed as a new category of financial regulation. Maybe in a few years capital markets derivatives and its margining is not only cash or liquid bonds, but plenty of additional securities from Bitcoin to small cap stocks.

For us, the recent regulatory developments empower further the business opportunities within the financial industry.