Numerix Tackles Trade Profitability with XVA Stack
New offering covers range of value adjustments
Underpinned by Numerix's CrossAsset Server models, Numerix XVA offers a range of counterparty credit risk measures including credit, debt, and funding valuation adjustments (CVA, DVA, and FVA) as well as potential future exposure (PFE), and expected positive and negative exposures (EPE and ENE).
Funding costs and capital calculations, including regulatory capital, CVA capital charges, cost of capital (KVA) and capital forecasting simulations can also be determined and leveraged to optimize capital usage, increase return on capital, and lower collateral costs.
“Numerix XVA can be used as part of a trade profitability framework that enables users to attribute portfolio-level risk, margin and capital analytics back to the trade. By getting the all-in profitability of the trade and observing all of the cost components, customers are empowered to make the most informed decision with respect to trading or risk managing derivatives," says Satyam Kancharla, SVP and chief strategy officer at Numerix.
The firm's expanding valuation adjustment framework was cited earlier this year as a central component in Numerix CrossAsset's Sell-Side Technology Best Credit Risk Product award.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: https://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Data Management
Spoiler alert: managing market data is a bad case for AI
The IMD Wrap: A recent conversation between Max and one of his sources highlights the uses of different mechanisms to manage one of their most expensive assets.
LSEG makes final case for dismissal of MayStreet lawsuit
Lawyers for both LSEG and MayStreet founder Patrick Flannery have argued the lawsuit’s merits through various legal filings for almost a year.
A new market data hope or an expanding Empire
Market data is now part of systemic infrastructure rather than just a commercial product. Tim Versteeg questions if market data is becoming too powerful to fail.
The race to ‘financialize’ GPU compute set to ratchet up
The Waters Wrap: Anthony looks at two companies aiming to bring efficiency and transparency to the GPU compute market.
Deutsche Börse invests $200M in Kraken, DTCC advances cloud strategy, and more
A recap of this week’s major tech and data news in the capital markets.
Data industry spend hits $50B for first time in new report
A new product by BCG Expand will track market data vendor size and market share as it seeks to show data users where the market is heading.
TNS integrates Radianz, Exegy reduces latency, BondXN allies with BlackRock, and more
A recap of this week’s major tech and data news in the capital markets.
Re-engineering reconciliations: User-initiated AI cuts recs from days to minutes
Reconciliations have long been tied to batch scheduling. Prasanna Anandan explains how one bank broke down bottlenecks by embedding an AI-driven, user-initiated interface.