Linear and nonlinear univariate models applied to forecasting financial series

André Alves Portela Santos*, Newton Carneiro Affonso Da Costa, Leandro Dos Santos Coelho

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


This work investigates the hypothesis that the nonlinear models of feedforward and radial basis function neural networks and the Takagi-Sugeno (TS) fuzzy system are able to provide a more accurate forecast than the traditional ARMA and ARMA-GARCH linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min., 60 min., 120 min., daily and weekly basis, the forecast performance is compared. Results indicate that forecast performance is related to the series' frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy, nonlinear models achieve higher returns when compared to a naive strategy and to the linear models.

Original languageEnglish
Publication statusPublished - 2005
EventIIE Annual Conference and Exposition 2005 - Atlanta, GA, United States
Duration: 14 May 200518 May 2005


ConferenceIIE Annual Conference and Exposition 2005
Country/TerritoryUnited States
CityAtlanta, GA


  • Forecasting
  • Linear models
  • Nonlinear models


Dive into the research topics of 'Linear and nonlinear univariate models applied to forecasting financial series'. Together they form a unique fingerprint.

Cite this