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In this paper we compare the modeling performance of three different radial basis function (RBF) artificial neural networks (ANN) when applied to the prediction of FX futures options volatility. We focus the analysis on the reported comparative advantages of the prior information Bayesian regularization-based Kajiji-4 RBF algorithmic structure in a study of(More)
The importance of volatility modeling is evidenced by the voluminous literature on temporal dependencies in financial market assets. A substantial body of this literature relies on explorations of daily and lower frequencies using parametric ARCH or stochastic volatility models. In this research we compare the model performance of alternate neural network(More)
Volatility modeling is the lifeline of the derivative-and asset-pricing evaluation process. As such, it is understandable that a voluminous literature has evolved to discuss the temporal dependencies in financial market volatility. Much of this literature has been directed at daily and lower frequencies using ARCH and stochastic volatility type models. With(More)
This document is not a final research report. It is a preliminary version of the final report. In addition to a review of research theory, the final report will include a software CD, software instructions, and a summary of less efficient findings that are purposely omitted in this short-form report. Report is provided for discussion purposes only.
This working paper series is intended to facilitate discussion and encourage the exchange of ideas. Inclusion here does not preclude publication elsewhere. It is the original work of the author(s) and subject to copyright regulations. course, the authors are solely responsible for any remaining errors. Abstract This paper provides evidence from hedge fund(More)