Empirical of the Taiwan stock index option price forecasting model – applied artificial neural network

2009 ◽  
Vol 41 (15) ◽  
pp. 1965-1972 ◽  
Author(s):  
Chin-Tsai Lin ◽  
Hsin-Yi Yeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Option Price ◽  
Stock Index ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Artificial Neural ◽  
Index Option

Analysis of Stock Volatility Clustering Using ANN

2015 ◽  
Vol 28 (2) ◽  
pp. 32-45 ◽  
Author(s):  
Manish Kumar ◽  
Santanu Das ◽  
Sneha Govil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Building ◽  
Stochastic Volatility Model ◽  
Propagation Model ◽  
Stock Index ◽  
Support Vector ◽  
Stock Volatility ◽  
Volatility Model ◽  
Artificial Neural

The model building theories broadly categorize the stock index forecasting models into two broad categories: Based on statistical theory consisting models such as Stochastic Volatility model (SV) and General Autoregressive Conditional Heteroskedasticity (GARCH) whereas other one based on artificial intelligence based models, such as artificial neural network (ANN), the support vector machine (SVM) and technique used for optimization such as particle swarm optimization (PSO). In existing literature, many of the statistical models when compared with artificial neural network models were outperformed by these models. This paper analyses stock volatility using ANN models as Multilayer perceptron with back propagation model and Radial Basis function.

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