Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/13747
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gradojevic N. | en |
dc.contributor.author | Kukolj, Dragan | en |
dc.date.accessioned | 2020-03-03T14:53:34Z | - |
dc.date.available | 2020-03-03T14:53:34Z | - |
dc.date.issued | 2011-09-15 | en |
dc.identifier.issn | 1672789 | en |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/13747 | - |
dc.description.abstract | Non-parametric option pricing models, such as artificial neural networks, are often found to outperform their parametric counterparts in empirical option pricing exercises. In this context, non-parametric models are viewed as more flexible and amenable to adaptive learning. However, the main drawback of non-parametric approaches is their lack of stability, which is detrimental to out-of-sample performance. This is the key reason why one may prefer a parsimonious parametric model. This paper proposes a parametric TakagiSugenoKang (TSK) fuzzy rule-based option pricing model that requires only a small number of rules to describe highly complex non-linear functions. The findings for this data-driven approach indicate that the TSK model presents a robust option pricing tool that is superior to an array of well-known parametric models from the literature. In addition, its predictive performance is consistently no worse than that of a non-parametric feedforward neural network model. © 2011 Elsevier B.V. All rights reserved. | en |
dc.relation.ispartof | Physica D: Nonlinear Phenomena | en |
dc.title | Parametric option pricing: A divide-and-conquer approach | en |
dc.type | Journal/Magazine Article | en |
dc.identifier.doi | 10.1016/j.physd.2011.07.001 | en |
dc.identifier.scopus | 2-s2.0-80052934340 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/80052934340 | en |
dc.relation.lastpage | 1535 | en |
dc.relation.firstpage | 1528 | en |
dc.relation.issue | 19 | en |
dc.relation.volume | 240 | en |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Departman za računarstvo i automatiku | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
Appears in Collections: | FTN Publikacije/Publications |
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