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Published July 2001 | Published
Journal Article Open

Financial model calibration using consistency hints


We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.

Additional Information

"© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." Manuscript received December 30, 2000; revised March 26, 2001; Posted online: 2002-08-07. This work was supported by the Center for Neuromorphic Systems Engineering, an Engineering Research Center supported by the National Science Foundation under NSF Cooperative Agreement EEC 9402726. The author would like to acknowledge Dr. M. Magdon-Ismail for his useful hints and to thank the members of Caltech's Learning Systems Group for helpful discussions.

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