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Financial model calibration using consistency hints.

Y S Abu-Mostafa¹

¹Learning Systems Group, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

IEEE Transactions on Neural Networks

|February 6, 2008

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This study presents a novel hint-based calibration technique for financial models, transforming curve fitting into genuine calibration for valid parameter inference. The method successfully calibrated interest rate models for Japanese Yen and US Dollar markets.

Area of Science:

Quantitative Finance
Financial Modeling
Econometrics

Background:

Traditional financial model calibration often struggles to ensure parameter validity.
Simple curve fitting lacks the robustness for inferring broad conclusions from parameters.
Existing methods may not adequately incorporate domain-specific knowledge or constraints.

Purpose of the Study:

To introduce a novel technique for forcing the calibration of financial models to yield valid parameters.
To enhance curve fitting into genuine calibration by incorporating learning from hints.
To enable broader inferences from model parameter values.

Main Methods:

Augmenting the curve fitting error function with consistency hint error functions.
Utilizing the Kullback-Leibler distance for hint error calculations.

Related Experiment Videos

Developing an efficient Expectation-Maximization (EM)-type optimization algorithm tailored to the technique.

Introducing and balancing various consistency hints using canonical errors.

Main Results:

Successfully converted simple curve fitting into genuine calibration.
Developed and implemented an efficient EM-type optimization algorithm.
Demonstrated the technique's efficacy by calibrating the correlated multifactor Vasicek model of interest rates.
Achieved successful application in the Japanese Yen swaps and US Dollar yield markets.

Conclusions:

The proposed hint-based calibration technique ensures the production of valid financial model parameters.
The method facilitates robust inference from parameter values, enhancing model interpretability.
The technique is effective for complex interest rate models and applicable to real-world financial markets.