Using neural networks to forecast the S&amp;P 100 implied volatility

Malliaris, Mary; Salchenberger, Linda

doi:10.1016/0925-2312(95)00019-4

Cited by 65 publications

(43 citation statements)

References 13 publications

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“…According to Malliaris and Salchenberger [19], NNs present the relationship between inputs and outputs using the architecture of human brain to process large information and detect patterns by interconnecting and organizing them in different layers for information processing purposes. These layers are formed by a set of processing elements or neurons.…”

Section: Volatilitymentioning

confidence: 99%

Sentiment and stock market volatility predictive modelling — A hybrid approach

Olaniyan

Stamate

Ouarbya

et al. 2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Abstract-The frequent ups and downs are characteristic to the stock market. The conventional standard models that assume that investors act rationally have not been able to capture the irregularities in the stock market patterns for years. As a result, behavioural finance is embraced to attempt to correct these model shortcomings by adding some factors to capture sentimental contagion which may be at play in determining the stock market. This paper assesses the predictive influence of sentiment on the stock market returns by using a non-parametric nonlinear approach that corrects specific limitations encountered in previous related work. In addition, the paper proposes a new approach to developing stock market volatility predictive models by incorporating a hybrid GARCH and artificial neural network framework, and proves the advantage of this framework over a GARCH only based framework. Our results reveal also that past volatility and positive sentiment appear to have strong predictive power over future volatility.

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Section: Volatilitymentioning

confidence: 99%

Sentiment and stock market volatility predictive modelling — A hybrid approach

Olaniyan

Stamate

Ouarbya

et al. 2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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“…Malliaris et al presented that the back propagation outperforms even Black. Shore, a popular statistical model in business management science, in predicting S & P implied volatility [13]. Levin proposed back propagation as the best approach to selection of beneficial stock [12].…”

Section: Previous Workmentioning

confidence: 99%

The effect of mid-term estimation on back propagation for time series prediction

2010

Neural Comput & Applic

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This research proposes estimation of mid-terms in a time series for improving the prediction performance of back propagation. In this research, the process of estimating mid-terms is called VTG (virtual term generation) and schemes for doing that are called VTG schemes. This research proposes three VTG schemes: mean method, 2nd Lagrange method, and 1st Taylor method. We adopt only back propagation as prediction model, since the goal of this research is to improve its prediction performance and back propagation is used most popular for regression among supervised neural networks. By implementing the VTG schemes as preprocessing of time series prediction, it will be observed that the prediction performance of back propagation is improved through experiments of Sect. 5.

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“…This approach is effective for input and output relationship modeling even for noisy data, and has been demonstrated to effectively model nonlinear relationships. Studies on derivative securities pricing using neural network have attracted researchers and practitioners, and they applied the neural network model and obtained better results than using the traditional stochastic model (Hutchinson et al 1994;Malliaris and Salchenberger 1996;Qi 1999;Yao et al 2000;Amilon 2003;Binner et al 2005;Lin and Yeh 2005).…”

mentioning

confidence: 99%