Volatility forecast using hybrid Neural Network models

被引：112

作者：

Kristjanpoller, Werner ^{[1
]}

Fadic, Anton ^{[1
]}

Minutolo, Marcel C. ^{[2
]}

机构：

[1] Univ Tecn Federico Santa Maria, Dept Ind, Valparaiso, Chile

[2] Robert Morris Univ, Dept Management, Moon Township, PA 15108 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2014年 / 41卷 / 05期

关键词：

Artificial Neural Networks; GARCH models; Risk forecast; Emerging markets; Latin; American stock markets; RETURNS;

D O I：

10.1016/j.eswa.2013.09.043

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico. A detail of the methodology and application of the volatility forecast of financial series using a hybrid artificial Neural Network model are presented. The results demonstrate that the ANN models can improve the forecasting performance of the GARCH models when studied in the three Latin-American markets and it is shown that the results are robust and consistent for different ANN specifications and different volatility measures. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：2437 / 2442

页数：6

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