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 e...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 12; no. 4; pp. 791 - 808
Main Author Abu-Mostafa, Y.S.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2001
Subjects
Calibration
Consistency
Curve fitting
Economic indicators
Entropy
Error functions
Finance
Helium
Markets
Mathematical models
Neural networks
Neuromorphics
Optimization
Steady-state
Systems engineering and theory
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Summary: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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ISSN:1045-9227
1941-0093
DOI:10.1109/72.935092