CompatibL was founded in 2003 by Alexander Sokol, winner of the Risk Quant of the Year Award and a seasoned technology executive who, prior to founding CompatibL, was founder and CTO of Numerix. Alexander continues to serve today as CompatibL’s Executive Chairman.
It is an old adage in the quant community that most quants can’t code, and most engineers can’t do math. From day one, CompatibL’s goal was to be the exception to this rule. We hire and train quants who also know a lot about software engineering, and we hire and train software engineers who have in-depth knowledge of math and quant models. Combining quant and engineering expertise in one team is what makes CompatibL so effective in developing software for the financial industry.
When other vendors build trading and risk solutions, more often than not the models and the application are developed separately and then fitted together. At CompatibL, we design our models from the ground up so that they work well within the application, and design the application so that it works well with the models.
Our software has won widespread industry recognition, including two back to back Risk Vendor of the Year Awards for our market and credit risk solution, CompatibL Risk.
CompatibL is an industry thought leader whose quantitative research program has produced multiple innovations in models and numerical methods for counterparty credit risk, settlement risk, risk premia in the yield curve, adjoint algorithmic differentiation, and many others.
Our collaboration with leading industry quants, regulators and academic scientists has resulted in many notable papers, including Exposure Under Systemic Impact by Alexander Sokol and Michael Pykhtin, Short-Rate Joint-Measure Models by John Hull, Alexander Sokol and Alan White (SSRN, Risk), and the 2018 Quant of the Year Award winning paper Rethinking the Margin Period of Risk (SSRN, Risk) by Leif Andersen, Michael Pykhtin and Alexander Sokol.
CompatibL’s research has been covered in many industry publications and editorials, including Time Trial: the Big Risks that Lurk in OTC Margin Gaps , CVA Models May Miss Half of True Default Risk and Time to Talk About Settlement Risk .
Alexander Sokol’s book Long-Term Portfolio Simulation (Risk Books, 2014) is the leading reference for the portfolio simulation techniques required for counterparty credit risk and limit models.
The research on Autoencoder Market Models for Interest Rates proposing a highly optimized latent factor representation of the yield curve using machine learning was published in December 2022.
Long-Term Portfolio Simulation
by Alexander SokolThe leading reference for the portfolio simulation techniques required for counterparty credit risk and limit models.
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