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A Hybrid Model Integrating Artificial Neural Network with Multiple GARCH-Type Models and EWMA for Performing the Optimal Volatility Forecasting of Market Risk Factors

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References

1986Generalized autoregressive conditional heteroskedasticity22,329 cited
1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
1995Multivariate Simultaneous Generalized ARCH4,510 cited
1989Why Does Stock Market Volatility Change Over Time?3,433 cited
1980The Extreme Value Method for Estimating the Variance of the Rate of Return1,946 cited
1994Multivariate Stochastic Variance Models1,367 cited
1990Alternative models for conditional stock volatility1,272 cited
1990Stationarity and Persistence in the GARCH(1,1) Model1,093 cited
2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models786 cited
1994Volatility and Links between National Stock Markets679 cited
1995A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada499 cited
2019The Impact of Machine Learning on Economics488 cited
2020Gold volatility prediction using a CNN-LSTM approach274 cited
1997An artificial neural network-GARCH model for international stock return volatility227 cited
2012Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?

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226 cited

2018A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis226 cited

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