Evolving hybrid neural fuzzy network for realized volatility forecasting with jumps

Rosa, Raul Arthur Fernandes; Maciel, Leandro; Gomide, Fernando; Ballini, Rosângela

doi:10.1109/cifer.2014.6924112

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…For comparison purposes, volatility forecasts are done using alternative evolving fuzzy systems, such as eTS [20], xTS [21], eTS+ [22], eCloud [9], and eHFN [10]. Moreover, we consider also a multi layer feedforward neural network.…”

Section: B Methodologymentioning

confidence: 99%

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Stock market volatility prediction using possibilistic fuzzy modeling

Maciel¹,

Gomide²,

Ballini³

2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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This paper addresses stock market assets return volatility forecasting and possibilistic fuzzy modeling. A recursive possibilistic fuzzy modeling (rPFM) approach is suggested to deal with the identification of systems affected by outliers and noisy data due to the use of memberships and typicalities to cluster data. Since financial markets are affected by news, expectations and investors psychology, the development of robust methodologies such as rPFM is essencial for market participants. The model is evaluated for volatility forecasting with jumps using intraday data of different stock markets. Results indicate that rPFM is a potential tool for volatility forecasting and outperforming some traditional recursive fuzzy and neural fuzzy models.

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Section: B Methodologymentioning

confidence: 99%

“…[9] and [10] suggested the use of a cloud-based evolving fuzzy model (eCloud) and a hybrid neural fuzzy network (eHFN) for volatility forecasting with jumps, respectively. The authors showed the high potential of evolving fuzzy modeling approaches in terms of accuracy, outperforming traditional econometric techniques.…”

Section: Introductionmentioning

confidence: 99%

Stock market volatility prediction using possibilistic fuzzy modeling

Maciel¹,

Gomide²,

Ballini³

2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

Self Cite

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“…These are nonlinear approaches that use lagged sample path and jump components from high-frequency data. For instance, [27] and [28] use a cloud-based evolving fuzzy model (eCloud) and a hybrid evolving neural fuzzy networks (eHFN), respectively, for volatility forecasting with jumps, and show that their accuracy are higher than traditional econometric techniques. Notably, the cloud-based evolving and neural fuzzy approaches embed a clustering mechanism that naturally captures volatility clustering stylized fact of financial time series.…”

Section: A Realized Volatility With Jumps Modelsmentioning

confidence: 99%

“…The ePFM is a non-linear, adaptive, and robust approach for realized volatility modeling. The results produced by ePFM are compared with the HAR [14] benchmark, with traditional artificial neural networks such as MLP and ANFIS, and with the evolving fuzzy models xTS [56], eTS+ [33], eCloud [27], and eHFN [28]. Comparisons are made using Value-at-Risk (VaR) estimates.…”

Section: Computational Experimentsmentioning

confidence: 99%

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

Maciel

Ballini

Gomide

2017

IEEE Trans. Fuzzy Syst.

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Equity assets volatility modeling and forecasting provide key information for risk management, portfolio construction, financial decision making and derivatives pricing. Realized volatility models outperform autoregressive conditional heteroskedasticity and stochastic volatility models in out-ofsample forecasting. Gain in forecasting performance is achieved when models comprise volatility jump components. This paper suggests evolving possibilistic fuzzy modeling to forecast realized volatility with jumps. The modeling approach is based on an extension of the possibilistic fuzzy c-means clustering and on functional fuzzy rule-based models. It employs memberships and typicalities to recursively update cluster centers. The evolving nature of the model allows adding or removing clusters using statistical distance-like criteria to update the model as dictated by input data. The possibilistic model improves robustness to noisy data and outliers, an essential requirement in financial markets volatility modeling and forecasting. Computational experiments and statistical analysis are done using Value-at-Risk estimates to evaluate and to compare the performance of the evolving possibilistic fuzzy modeling with the Heterogeneous Autoregressive, neural networks models and current state of the art evolving fuzzy methods. The experiments use actual data from S&P 500 and Nasdaq (United States), FTSE (United Kingdom), DAX (Germany), IBEX (Spain) and Ibovespa (Brazil), major equity market indexes in global markets. The results show that the evolving possibilistic fuzzy model is highly efficient to model realized volatility with jumps in terms of forecasting accuracy.

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“…However, there is no direct volatility forecasts, based on literature. Moreover, NN seem to provide accurate realized volatility forecasts(Rosa et al, 2014). However, there is no direct link between each parameter selection and the properties of realized volatilities.…”

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confidence: 99%

Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH

Vortelinos

2017

Research in International Business and Finance

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Evolving hybrid neural fuzzy network for realized volatility forecasting with jumps

Cited by 15 publications

References 33 publications

Stock market volatility prediction using possibilistic fuzzy modeling

Stock market volatility prediction using possibilistic fuzzy modeling

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH

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