Spire has partnered with a leading financial valuations and risk firm to offer a unique, quantitative credit-risk prediction model that accounts for global systemic complexity and the ‘real-life’ world of possible outcomes.
This is particularly useful for investment and private banks, asset managers, insurers and family offices that hold large portfolios of bonds and wish to significantly improve their ability to price these assets, as well as predict default risk. It addresses the problem of most quantitative models still assuming normally distributed returns and correlation structures when pricing assets.
Our approach leverages Machine Learning, A.I. and stochastic calculus in order to project corporate financials and value each and every tranche of its capital, and is borne out of a combined century-long experience in distressed debt investments, investment banking and structured equity derivatives.
We can provide tailor-made products ranging from reports on corporate borrowers to subscription-access to a database containing data on over 5,000 corporates (including data on asset and asset return distribution, current and past Point-in-Time multiyear credit attributes and powerful insights into issues of credit dependencies).