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Volatility forecast using hybrid Neural Network models

Affiliation:	1. Departamento de Industrias, Universidad Técnica Federico Santa María, Chile, Av. España 1680, Valparaíso, Chile;2. Department Management, Robert Morris University, 324 Massey 6001 University Blvd Moon Township, PA 15108, USA;1. CMR Institute of Technology, AECS Layout, Bangalore, Karnataka 560037, India;2. Christ University, Hosur Road, Bangalore, Karnataka 560029, India;1. Department of Statistics, National Cheng Kung University, Tainan 70101, Taiwan, ROC;2. Department of Applied Mathematics, National Chiayi University, Chiayi 60004, Taiwan, ROC;1. Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore, Singapore;1. College of Computer Science, Chongqing University, Chongqing 400044, China;2. College of Software Engineering, Chongqing University, Chongqing 400044, China;3. HuaWei Research Institute, Chengdu, Sichuan 610041, China

Abstract:	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.

Keywords:	Artificial Neural Networks GARCH models Risk forecast Emerging markets Latin American stock markets
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