NDLI: Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns

Content Provider	Springer Nature Link
Author	Chen, Shiyi Jeong, Kiho Härdle, Wolfgang K.
Copyright Year	2014
Abstract	Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR based ARMA model is compared with five competing models (random walk, threshold ARMA model, MLE based ARMA model, recurrent artificial neural network based ARMA model and feed-forward SVR based ARMA model) by using two forecasting accuracy evaluation metrics (NSME and sign) and robust Diebold–Mariano test. The results reveal that for one-step-ahead forecasting, the recurrent SVR model is consistently better than the benchmark models in forecasting both the magnitude and turning points, and statistically improves the forecasting performance as opposed to the usual feed-forward SVR.
Starting Page	821
Ending Page	843
Page Count	23
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	30
Issue Number	3
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-11-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Recurrent support vector regression Non-linear ARMA Financial forecasting Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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