Financial model calibration using consistency hints
Abu-Mostafa, Y.S.
Learning Syst. Group, California Inst. of Technol., Pasadena, CA;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Jul 2001
Volume: 12,
Issue: 4
On page(s): 791-808
ISSN: 1045-9227
References Cited: 19
CODEN: ITNNEP
INSPEC Accession Number: 6997302
Digital Object Identifier: 10.1109/72.935092
Current Version Published: 2002-08-07
Abstract
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
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