Forecasting the Volatility of Stock Price Index

Roh, Tae Hyup

doi:10.1007/11811305_47

Cited by 47 publications

(51 citation statements)

References 18 publications

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“…Kim and Han [12] proposed genetic algorithms approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Roh [13] integrated neural network and time-series model for forecasting the volatility of stock price index. Thawornwong and Enke [19] proposed redeveloped neural network models for predicting the directions of future excess stock return.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

Wei¹

2015

JOEBM

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Section: Related Workmentioning

confidence: 99%

A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

Wei¹

2015

JOEBM

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“…They found that forecasts from neural networks outperformed implied volatility forecasts and that those forecasts were not significantly different from realized volatility. Roh (2007) found that NN-EGARCH showed a good performance when compared with the NN and NN-GARCH model to forecast the KOPSI volatility. Bildirici and Ersin (2009) combined ARCH/GARCH family models with artificial neural networks to predict daily volatility of Istanbul Stock Exchange.…”

Section: Introductionmentioning

confidence: 97%

“…Indeed, volatility and volume series are nonlinear and it is appropriate to approximate their relationship using nonlinear intelligent techniques such as artificial neural networks. In addition, BPNN has proven its capability to outperform traditional GARCH family models in the prediction of volatility (Hamid & Iqbal, 2004, Roh, 2007, Bildirici & Ersin, 2009Hung, 2011, Wang et al, 2011, Hajizadeh, 2012.…”

Section: Introductionmentioning

confidence: 99%

An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume

Lahmiri¹

2012

MSL

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In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatility series were estimated using the Exponential Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. The simulation results show that trading volume helps improving the forecasting accuracy of BPNN in Morocco but not in Saudi Arabia. As a result, volume represents valuable information flow to be used in the modeling and prediction of volatility in Morocco. In addition, it is found that BPNN overpredicts volatility during high volatile periods. This finding is important in financial applications such as asset allocation and derivatives pricing.

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“…Since interacting particle systems consist of a large number of interacting units, we think that economic systems such as financial markets are similar to interacting particle systems in that they are comprised of [18][19][20][21][22][23][24][25] . ANN has good self-learning ability, strong anti-jamming capability, has been widely used in the financial fields such as stock prices, profits, exchange rate, risk analysis and prediction.…”

Section: Introductionmentioning

confidence: 99%

Complex System Analysis on Voter Stochastic System and Jump Time Effective Neural Network of Stock Market

Wang¹,

Pan²,

Wang³

et al. 2015

IJCIS

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The finite-range voter system, one of stochastic particle systems, is applied to model a financial price process for further description and investigation of fluctuations of Shanghai Composite Index. For different parameter values of the intensity and the range R , we investigate the statistical behaviors of the simulation data for this financial model. Then we develop the random jump time effective neural network model to forecast the fluctuations of Shanghai Composite Index. Moreover, we compare the two models by analyzing the returns and the absolute returns of Shanghai Composite Index, the simulation data and the predictive data through Detrended Fluctuation Analysis and classic Rescaled Range Analysis.

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Forecasting the Volatility of Stock Price Index

Cited by 47 publications

References 18 publications

A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

A Hybrid Model Based on ANFIS and Empirical Mode Decomposition for Stock Forecasting

An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume

Complex System Analysis on Voter Stochastic System and Jump Time Effective Neural Network of Stock Market

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