|
EconStor >
Deutsche Bank Research, Frankfurt am Main >
Research Notes, Deutsche Bank Research >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/40277
|
| | |
| Title: | | Feature extraction with hybrid neural networks  |
| Authors: | | Wegmann, Georg |
| Issue Date: | | 2000 |
| Series/Report no.: | | Research notes in economics & statistics 00-6 |
| Abstract: | | Neural networks (NN) and fuzzy logic systems (FLS) are used successfully for financial forecasting, credit rating and portfolio management. In search for more sophisticated modeling techniques a mixture of NN and FLS has proved to be worth consideration. We propose the novel constructive approach by which a neuro fuzzy network is built up with the help of a constrained optimizer. The mathematical motivation for such hybrid networks is presented, using the Kolmogorov theory of metric entropy. As an application of the proposed approach we build a neuro fuzzy network model which is able to explain the prices of call options written on the S&P 500 stock index. While option pricing theory typically requires a highly complex statistical model to capture the empirical pricing mechanism, our results indicate that this algorithm leads to more parsimonious functional specificationes which have a superior out-of-sample performance. |
| Subjects: | | neural networks fuzzy logic systems entropy option pricing |
| Document Type: | | Working Paper |
| Appears in Collections: | | Research Notes, Deutsche Bank Research
|
| |
| | |
Download bibliographical data as:
BibTeX
|
| |
Share on:http://hdl.handle.net/10419/40277
|
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.
|