A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Maciel, Leandro

doi:10.1057/9781137025098_11

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…They used the GJR-GARCH model to estimate the extreme tail risk. Moreover, Maciel (2013) applied a similar methodology in the stock market. As cryptocurrencies suffer from fat-tail risk, we prefer to implement the GJR-GARCH model to accurately estimate the volatility of cryptocurrencies.…”

Section: Literature Reviewmentioning

confidence: 99%

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Mostafa

Saha

Islam

et al. 2021

JRFM

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Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

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Section: Literature Reviewmentioning

confidence: 99%

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Mostafa

Saha

Islam

et al. 2021

JRFM

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“…The authors concluded that those forecasts obtained from GK-ARMA models are in general as good as those obtained from more complex GARCH models. More examples of previous literature in Brazil are Santanda and Bueno 2008, Woo et al (2009), Mendes and Accioly (2012), Maciel (2012), and Vicente et al (2012).…”

Section: Empirical Literature Reviewmentioning

confidence: 99%

Forecasting USD-BRL Currency Rate Volatility using Realized and Implied Volatilities Data

Campani

Duraes

2018

Estud. Econ.

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This article assesses the impact of exogenous variables in GARCH models, when applied to volatility forecasts for the Brazilian USD-BRL currency market. As exogenous variables, we used the realized variance, based on high frequency data, and the FXVol index, based on market implied volatility data. This is the first study to use the FXVol index and to investigate its effects on Brazilian foreign exchange volatility. The results indicate statistical significance of the superiority of the extended models when predicting volatility. We conclude that high frequency data and market implied volatility contain relevant information with respect to USD-BRL currency volatility. These find ings are relevant for hedgers, speculators and practitioners in general.

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“…e hybrid fuzzy time series models proposed in references [10,11] have shown important enhancements in forecasting stock market volatility, outperforming the traditional time series models, neural networks, other hybrid models, etc. Adaptive neurofuzzy information system (ANFIS) is a different popular hybrid model used in volatility forecasting [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System

Mohammed

Aduda

Kube

2020

Journal of Mathematics

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This paper proposes an innovative semiparametric nonlinear fuzzy-EGARCH-ANN model to solve the problem of accurate modeling for forecasting stock market volatility. This model has been developed by a combination of the FIS, ANN, and EGARCH models. Because the proposed model is highly nonlinear and gradient-based parameter estimation methods might not give global optimal parameters for highly nonlinear models, the study has decided to use evolutionary algorithms instead. In particular, a differential evolution (DE) algorithm is suggested to solve the parameter estimation problem of the proposed model. After this, the semiparametric nonlinear fuzzy-EGARCH-ANN model has been developed mathematically from the three models mentioned before, and the study has simulated data by it. After the simulation, parameter estimation of the proposed model using a differential evolution algorithm on the simulated data is done. Finally, it is seen that the proposed model is good in capturing the volatility clustering and leverage effects of highly nonlinear and complicated financial time series data that were overlooked by the EGARCH model.

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A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Cited by 6 publications

References 56 publications

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Forecasting USD-BRL Currency Rate Volatility using Realized and Implied Volatilities Data

Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System

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