Results 141 to 150 of about 36,176 (157)
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Energy, 2019
We study the dynamic relationship between the global oil market and China's commodity market at the industry level by using a DCC-GJR-GARCH model. Results of this study reveal strong return spillovers and the long-term time-varying linkages in volatility
Yonghong Jiang +3 more
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We study the dynamic relationship between the global oil market and China's commodity market at the industry level by using a DCC-GJR-GARCH model. Results of this study reveal strong return spillovers and the long-term time-varying linkages in volatility
Yonghong Jiang +3 more
semanticscholar +1 more source
Approximating the GJR-GARCH and EGARCH option pricing models analytically
The Journal of Computational Finance, 2006In Duan, Gauthier and Simonato (1999), an analytical approximate formula for European options in the GARCH framework was developed. The formula is however restricted to the nonlinear asymmetric GARCH model. This paper extends the same approach to two other important GARCH specifications GJR-GARCH and EGARCH.
Jin-Chuan Duan +3 more
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GJR-GARCH model in value-at-risk of financial holdings
Applied Financial Economics, 2011In this study, we introduce an asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, Glosten, Jagannathan and Runkle-GARCH (GJR-GARCH), in Value-at-Risk (VaR) to examine whether or not GJR-GARCH is a good method to evaluate the market risk of financial holdings.
Y. C. Su, H. C. Huang, Y. J. Lin
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Building Fuzzy Levy-GJR-GARCH American Option Pricing Model
2019Taking into account the time-varying, jump and leverage effect characteristics of asset price fluctuations, we first obtain the asset return rate model through the GJR-GARCH model (Glosten, Jagannathan and Rundle-generalized autoregressive conditional heteroskedasticity model) and introduce the infinite pure-jump Levy process into the asset return rate
Huiming Zhang, Junzo Watada
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Semiparametric efficient adaptive estimation of the GJR-GARCH model
Statistics & Risk Modeling, 2018Abstract In this paper we derive a semiparametric efficient adaptive estimator for the GJR-GARCH ( 1 , 1 )
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On a GJR-GARCH Model with the Standardized Pearson Type IV Distribution
SSRN Electronic Journal, 2013We examine the efficiency of a GJR-GARCH model where the residuals follow the standardize Pearson type-IV distribution. As a case study we consider the historical daily close price of the Standard and Poor’s index. The model is tested with a variety of loss functions and the efficiency is examined by application of several Value-at-Risk tests and ...
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SSRN Electronic Journal, 1999
This article proposes a portfolio-based pricing method to evaluate risk and systematically consider risk premium. The risk premium is charged to satisfy risk management and return on risk capital requirements. The P&L distributions are priced based on Value-at-Risk and return on capital approach.
Dajiang Guo, Sergei E. Esipov
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This article proposes a portfolio-based pricing method to evaluate risk and systematically consider risk premium. The risk premium is charged to satisfy risk management and return on risk capital requirements. The P&L distributions are priced based on Value-at-Risk and return on capital approach.
Dajiang Guo, Sergei E. Esipov
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Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach
Expert Systems with Applications, 2009This 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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A Modified GJR-GARCH Model with Information Disseminating Speed
2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007), 2007Guo Qing Zhao, Jun Wei
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