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Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility
10 references·6 citing·28 second hop
Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility (2011)Applying a combined fuzzy systems and GAR…Hybridization of intelligent techniques and ARIMA models for time series prediction (2007)Hybridization of intelligent …A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting (2006)A hybrid modular neural netwo…Volatility forecasting with smooth transition exponential smoothing (2004)Volatility forecasting with s…A hybrid genetic-neural architecture for stock indexes forecasting (2003)A hybrid genetic-neural archi…Time series : applications to finance (2002)Time series : applications to…On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)On the Relation between the E…Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Conditional Heteroskedasticit…Generalized autoregressive conditional heteroskedasticity (1986)Generalized autoregressive co…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Autoregressive Conditional He…The Behavior of Stock-Market Prices (1965)The Behavior of Stock-Market …A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting (2018)A fuzzy neural network combin…ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting (2017)ProfARIMA: A profit-driven or…An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction (2016)An evolutionary hybrid Fuzzy …Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting (2015)Evolving Fuzzy-GARCH Approach…Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting (2013)Ensemble ANNs-PSO-GA Approach…Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting (2013)Ensemble ANNs-PSO-GA Approach…Nonlinear dynamics, chaos, and instability (1993)Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Multivariate Simultaneous Generalized ARCH (1995)Time Series Analysis: Forecasting and Control. (1978)Time series forecasting using a hybrid ARIMA and neural network model (2003)Development of a PSO–SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting (2009)A new class of hybrid models for time series forecasting (2011)A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting (2015)Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2008)Forecasting stock market short-term trends using a neuro-fuzzy based methodology (2009)The Econometrics of Financial Markets (2012)Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction (2018)DISTRIBUTION OF INCOMES OF CORPORATIONS AMONG DIVIDENDS, RETAINED EARNINGS AND TAXES (1956)Portfolio Selection: Efficient Diversification of Investments (1959)Dividend Policy, Growth, and the Valuation of Shares (1961)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Risk, Return, and Equilibrium: Empirical Tests (1973)Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models (2009)Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods (2009)Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction (2019)Econometric Analysis of Cross Section and Panel Data (2001)ARCH modeling in finance (1992)Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles (2000)The Cross‐Section of Volatility and Expected Returns (2006)A Note on Non‐Negative Arma Processes (2006)A Fuzzy Asymmetric GARCH model applied to stock markets (2009)A fuzzy GARCH model applied to stock market scenario using a genetic algorithm (2009)
this work references cited by second hop

References

  • 1986Generalized autoregressive conditional heteroskedasticity22,329 cited
  • 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
  • 1991Conditional Heteroskedasticity in Asset Returns: A New Approach10,268 cited
  • 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks9,041 cited
  • 1965The Behavior of Stock-Market Prices8,707 cited
  • 2003A hybrid genetic-neural architecture for stock indexes forecasting320 cited
  • 2007Hybridization of intelligent techniques and ARIMA models for time series prediction176 cited
  • 2004Volatility forecasting with smooth transition exponential smoothing122 cited
  • 2006A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting83 cited
  • 2002Time series : applications to finance62 cited

Cited by

  • 2013Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting59 cited
  • 2017ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting40 cited
  • 2018A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting37 cited
  • 2013Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

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37 cited
  • 2016An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction36 cited
  • 2015Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting28 cited