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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 4, JULY 2001 791 Financial Model Calibration Using Consistency Hints
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 4, JULY 2001 791
Financial Model Calibration Using Consistency Hints
Yaser S. Abu-Mostafa
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
Kullback­Leibler distance. We introduce an efficient EM-type op-
timization 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 in-
terest rates, and apply it successfully to Japanese Yen swaps market
and U.S. Dollar yield market.
Index Terms--Canonical error, computational finance, consis-
tency hint, cross entropy, EM algorithm, financial engineering, in-
terest rates, Kullback­Leibler distance, model calibration, multi-
factor models, optimization, overfitting, Vasicek model, volatility
term structure.

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences