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Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach

Expert Systems with Applications, 2009
This study integrated new hybrid asymmetric volatility approach into artificial neural networks option-pricing model to improve forecasting ability of derivative securities price. Owing to combines the new hybrid asymmetric volatility method can be reduced the stochastic and nonlinearity of the error term sequence and captured the asymmetric volatility
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GJR-Copula-CVaR Model for Portfolio Optimization: Evidence for Emerging Stock Markets

2018
Abstract T his paper empirically examines the impact of dependence structure between the assets on the portfolio optimization, composed of Tehran Stock Exchange Price Index and Borsa Istanbul 100 Index. In this regard, the method of the Copula family functions is proposed as powerful and flexible tool to determine the structure of
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A Modified GJR-GARCH Model with Information Disseminating Speed

2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007), 2007
Guo Qing Zhao, Jun Wei
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Kinerja Pencocokan Model Realized GJR-CJ pada Data Aset Keuangan

Volatilitas merupakan indikator utama dalam menilai risiko ketika membuat keputusan investasi. Di dunia pasar keuangan volatilitas mencerminkan tingkat fluktuasi nilai aset keuangan selama periode tertentu. Cara paling umum untuk mengukur potensi kerugian di masa depan dari suatu investasi adalah melalui volatilitas.
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Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models

2023
Sarbjit Singh   +2 more
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GJR-GARCH process with normal errors of varying mean

Communications in Statistics - Simulation and Computation
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