
NumeriX enhanced to support more complex credit products
NumeriX's analytics kit allows institutional users to develop customised applications. Its NX CR engine is an off-the-shelf software product for pricing and risk management of a broad range of credit products.
The company's newly enhanced engine allows risk management of instruments including single-name exotics, basket structures with multiple valuation models and sophisticated hybrid products. There is also support for conventional waterfalls for cash and synthetic collateralised debt obligations.
NumeriX uses both industry standard and proprietary models for its Credit Tools and NX CR Engine. Among these are one-dimensional lognormal mean-reverting models; two-dimensional models to diffuse spreads and interest rates for basket structures and credit portfolios; quasi multi-period models; copula function models; true multi-period models; CDO models; and semi-analytic limiting distribution models.
RiskNews reported a tie-up between NumeriX and UK data pricing service provider Mark-it Partners in February. That deal focused in part on developing CDO pricing services. Mark-it and NumeriX linked to identify suitable cross-asset correlation models.
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@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Technology
Chartis RiskTech100® 2024
The latest iteration of the Chartis RiskTech100®, a comprehensive independent study of the world’s major players in risk and compliance technology, is acknowledged as the go-to for clear, accurate analysis of the risk technology marketplace. With its…
T+1: complacency before the storm?
This paper, created by WatersTechnology in association with Gresham Technologies, outlines what the move to T+1 (next-day settlement) of broker/dealer-executed trades in the US and Canadian markets means for buy-side and sell-side firms
Empowering risk management with AI
This webinar explores how artificial intelligence (AI) can strip out the overheads and effort of rapidly modelling, monitoring and mitigating risk
Core-Payments for business leaders: why real-time access to payment data is key to long‑term business success
Business leaders require easy access to timely, reliable and complete information across post-trade processes. Aside from the usual requirements of senior managers to optimise for risk, revenues and costs, they increasingly need to demonstrate to their…
Risk applications and the cloud: driving better value and performance from key risk management architecture
Today's financial services organisations are increasingly looking to move their financial risk management applications to the cloud. But, according to a recent survey by Risk.net and SS&C Algorithmics, many risk professionals believe there is room for…
Machine learning models: the validation challenge
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?
Banks strive for machine learning at quantum speed
Embryonic work on quantum neural networks raises hope of faster, more accurate models