Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/13747
Title: | Parametric option pricing: A divide-and-conquer approach | Authors: | Gradojevic N. Kukolj, Dragan |
Issue Date: | 15-Sep-2011 | Journal: | Physica D: Nonlinear Phenomena | 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. | URI: | https://open.uns.ac.rs/handle/123456789/13747 | ISSN: | 1672789 | DOI: | 10.1016/j.physd.2011.07.001 |
Appears in Collections: | FTN Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
4
checked on Sep 14, 2022
Page view(s)
27
Last Week
14
14
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.