Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach

By Yi-Hsien Wang. 2009

145 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Arash Bahrammirzaee
2. Mingyue Qiu
3. Yu Song
4. Saman Haratizadeh
5. Ehsan Hoseinzade

In Electrical and Electronic Engineering

1. Arash Bahrammirzaee
2. Mingyue Qiu
3. Yu Song

In Management Science and Operations Research

1. Arash Bahrammirzaee
2. Mingyue Qiu
3. Yu Song
4. Saman Haratizadeh
5. Ehsan Hoseinzade
6. Jacinta Chan Phooi M’ng
7. M. Mehralizadeh
8. Anthony Grace

Citations in the corpus

• Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model (2016, background): “The network weights and biases are assigned initial values first, and the error between the predicted and actual output values is back-propagated via the network for updating the weights and biases repeatedly [28].”
• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[219] Forecasting model for stock index option price GJR-GARCH and ANN GARCH and conventional GJR– GARCH Hybrid model performs better”