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Abstract: Billions of dollars flow through the world's financial markets every day, and market participants are understandably eager to accurately price financial instruments and understand relationships involving them. Nonlinear multivariate statistical modeling on fast computers offers the potential to capture more of the underlying dynamics of these high dimensional, noisy systems than traditional models while at the same time making fewer restrictive assumptions about them. For this style of... (Update)
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.... type of local learning algorithm that relies on prototypes is the radial basis function (RBF) network [Poggio and Girosi, 1990; Hutchinson, 1993; Wasserman, 1993] RBF networks are a function approximation method in which Gaussian or other basis functions are located at a...
.... et al. 1991 ] Radial basis function (RBF) networks also provide an analogy for prototype selection [ Poggio and Girosi, 1990; Hutchinson, 1993; Wasserman, 1993 ] RBF networks are a function approximation method in which Gaussian or other basis functions are located at a...
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BibTeX entry: (Update)
Hutchinson, James M. (1994) A radial basis function approach to financial time series analysis Ph.D. thesis, Massachusetts Institute of Technology. http://citeseer.ist.psu.edu/article/hutchinson94radial.html More
@techreport{ hutchinson93radial,
author = "James M. Hutchinson",
title = "A Radial Basis Function Approach to Financial Time Series Analysis",
number = "AITR-1457",
pages = "160",
year = "1993",
url = "citeseer.ist.psu.edu/article/hutchinson94radial.html" }
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