{"id":540,"date":"2023-05-02T11:23:54","date_gmt":"2023-05-02T11:23:54","guid":{"rendered":"https:\/\/nag.com\/?post_type=case-studies&p=540"},"modified":"2023-07-05T08:59:15","modified_gmt":"2023-07-05T08:59:15","slug":"investment-company-calibrate-nonlinear-least-squares-problem-with-nag-library-optimization-solvers","status":"publish","type":"case-studies","link":"https:\/\/nag.com\/case-studies\/investment-company-calibrate-nonlinear-least-squares-problem-with-nag-library-optimization-solvers\/","title":{"rendered":"Investment Company Calibrate Nonlinear Least Squares Problem with n<\/i><\/span>AG\u00ae Library Optimization Solvers"},"content":{"rendered":"\n
\n
\n
\n
\n
\n
\n \n

Investment Company Calibrate Nonlinear Least Squares Problem with n<\/i><\/span>AG\u00ae Library Optimization Solvers<\/h1>\n \n

Case Study<\/p>\n<\/div>\n\n \n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n

<\/div>\n\n
\n
\n
\n

Global Investment Company Exane use\u00a0n<\/i><\/span>AG\u00ae Library<\/a>\u00a0optimization solvers to calibrate parametric arbitrage-free volatility surfaces. After a testing phase including benchmarking n<\/i><\/span>AG solvers against other numerical libraries, and open-source alternatives, the\u00a0n<\/i><\/span>AG\u00ae Library<\/a>\u00a0was selected for its performance, efficiency, and reliability.<\/p>\n

Exane specialize in three finance areas: Cash Equities, Derivatives and Asset Management, and it was within the Equity Derivatives function that Exane benefitted from using n<\/i><\/span>AG\u2019s superior optimization solvers.<\/p>\n

The Challenge<\/h3>\n

The Exane Quant Team for Equity Derivatives needed to quickly, efficiently and, on a continuous basis, solve a constrained nonlinear least squares optimization problem with approximately 50 parameters, 100 linear constraints and 100 nonlinear constraints.<\/p>\n

The Solution<\/h3>\n

Having previously used\u00a0n<\/i><\/span>AG\u00ae Library<\/a>\u00a0routines in other applications, Exane team members selected n<\/i><\/span>AG\u00ae Library optimization routines and conducted an extensive test phase, including pitching them against other numerical libraries and several open-source alternatives. During the testing phase n<\/i><\/span>AG helped the Exane team achieve a proof of concept, overcoming the initial complexity challenges. At evaluation finish n<\/i><\/span>AG was chosen to supply their solvers for a host of reasons including the extensive algorithmic coverage found in the\u00a0n<\/i><\/span>AG\u00ae Library<\/a>. The Library offers numerous algorithms for the same class of problems giving increased user choice when selecting the right solver for the problem. In addition n<\/i><\/span>AG delivered:<\/p>\n