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Predicting Stock Index Volatility Using Artificial Neural Networks: An empirical study of the OMXS30, FTSE100 & S&P/ASX200

Johnsson, Ola LU (2018) NEKN01 20181
Department of Economics
Abstract
In this thesis I study the performances of artificial neural networks (ANNs) and three various ARCH-type models to predict weekly volatility of the Swedish (OMXS30), the British (FTSE100) and the Australian (S&P/ASX200) major stock indices. The three various ARCH-type models are the GARCH(1,1), the EGARCH(1,1) and the TGARCH(1,1). The purpose is to investigate if ANNs outperform the more traditional ARCH-type models in predicting weekly stock index volatility. An out-of-sample testing methodology is applied to the most recent 20 percent of the data observations, which fully range from 8th February 2008 to 29th December 2017. The metrics used to evaluate the volatility-predicting performances of the different models are the RMSE, the MAE,... (More)
In this thesis I study the performances of artificial neural networks (ANNs) and three various ARCH-type models to predict weekly volatility of the Swedish (OMXS30), the British (FTSE100) and the Australian (S&P/ASX200) major stock indices. The three various ARCH-type models are the GARCH(1,1), the EGARCH(1,1) and the TGARCH(1,1). The purpose is to investigate if ANNs outperform the more traditional ARCH-type models in predicting weekly stock index volatility. An out-of-sample testing methodology is applied to the most recent 20 percent of the data observations, which fully range from 8th February 2008 to 29th December 2017. The metrics used to evaluate the volatility-predicting performances of the different models are the RMSE, the MAE, the MAPE and the out-of-sample sample R^2. The results show no evidence of ANN predicting superiority for any of the three stock indices. (Less)
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author
Johnsson, Ola LU
supervisor
organization
course
NEKN01 20181
year
type
H1 - Master's Degree (One Year)
subject
keywords
artificial neural networks, volatility, ARCH-type models
language
English
id
8946583
date added to LUP
2018-07-03 14:20:55
date last changed
2018-07-03 14:20:55
@misc{8946583,
  abstract     = {In this thesis I study the performances of artificial neural networks (ANNs) and three various ARCH-type models to predict weekly volatility of the Swedish (OMXS30), the British (FTSE100) and the Australian (S&P/ASX200) major stock indices. The three various ARCH-type models are the GARCH(1,1), the EGARCH(1,1) and the TGARCH(1,1). The purpose is to investigate if ANNs outperform the more traditional ARCH-type models in predicting weekly stock index volatility. An out-of-sample testing methodology is applied to the most recent 20 percent of the data observations, which fully range from 8th February 2008 to 29th December 2017. The metrics used to evaluate the volatility-predicting performances of the different models are the RMSE, the MAE, the MAPE and the out-of-sample sample R^2. The results show no evidence of ANN predicting superiority for any of the three stock indices.},
  author       = {Johnsson, Ola},
  keyword      = {artificial neural networks,volatility,ARCH-type models},
  language     = {eng},
  note         = {Student Paper},
  title        = {Predicting Stock Index Volatility Using Artificial Neural Networks: An empirical study of the OMXS30, FTSE100 & S&P/ASX200},
  year         = {2018},
}