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Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization
15 references·5 citing·34 second hop
Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization (2011)Adaptive Fuzzy-GARCH model applied to for…Hybridization of intelligent techniques and ARIMA models for time series prediction (2007)Hybridization of intelligent …Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching (2007)Forecasting volatility and vo…Analysis of Financial Time Series (2006)Analysis of Financial Time Se…A particle swarm optimization approach to the nonlinear resource allocation problem (2006)A particle swarm optimization…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…Analysis of Financial Time Series (2002)Analysis of Financial Time Se…Out-of-sample tests of forecasting accuracy: an analysis and review (2000)Out-of-sample tests of foreca…Fuzzy sets (1996)Fuzzy setsOn 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 …Fuzzy sets (1965)Fuzzy setsForecasting the overnight return direction of stock market index combining global market indices: A multiple-branch deep learning approach (2022)Forecasting the overnight ret…Volatility GARCH models with the ordered weighted average (OWA) operators (2021)Volatility GARCH models with …Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting (2015)Evolving Fuzzy-GARCH Approach…An adaptive local linear optimized radial basis functional neural network model for financial time series prediction (2015)An adaptive local linear opti…A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility (2015)A new NN-PSO hybrid model for…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Multivariate Simultaneous Generalized ARCH (1995)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)The M3-Competition: results, conclusions and implications (2000)Exponential smoothing: The state of the art—Part II (2006)A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2012)Forecasting: theory and practice (2022)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)Multivariate GARCH models: a surveyThe Pricing of Options and Corporate Liabilities (1973)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Modeling Ordered Choices (2010)THE EVOLUTION OF STOCK MARKET EFFICIENCY OVER TIME: A SURVEY OF THE EMPIRICAL LITERATURE (2010)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)Extensions of the TOPSIS for group decision-making under fuzzy environment (2000)An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field (2010)Science mapping software tools: Review, analysis, and cooperative study among tools (2011)Family resemblances: Studies in the internal structure of categories (1975)Basic objects in natural categories (1976)Soft set theory—First results (1999)
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  • 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
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  • 1965The Behavior of Stock-Market Prices8,707 cited
  • 2006Analysis of Financial Time Series2,348 cited
  • 2000Out-of-sample tests of forecasting accuracy: an analysis and review1,014 cited
  • 2003A hybrid genetic-neural architecture for stock indexes forecasting320 cited
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  • 2004Volatility forecasting with smooth transition exponential smoothing122 cited
  • 2002Analysis of Financial Time Series120 cited
  • 2007Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching101 cited
  • 2006A particle swarm optimization approach to the nonlinear resource allocation problem

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70 cited

Cited by

  • 2022Forecasting the overnight return direction of stock market index combining global market indices: A multiple-branch deep learning approach44 cited
  • 2015An adaptive local linear optimized radial basis functional neural network model for financial time series prediction43 cited
  • 2021Volatility GARCH models with the ordered weighted average (OWA) operators30 cited
  • 2015A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility29 cited
  • 2015Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting28 cited