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Value at Risk Prediction for the GJR-GARCH Aggregation Model
Pattimura International Journal of Mathematics (PIJMath), 2022Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH.
Ariestha Widyastuty Bustan +2 more
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Quantum prediction GJR model and its applications
Statistica Neerlandica, 2014In this paper, a new statistical method to deal with the quantum finance is proposed. Through analyzing the stock data of China Mobile Communication Corporation, we discover its quantum financial effect, and then we innovate the method of testing the existence of the quantum financial effect.
Feixing Wang, Yingshuai Wang
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Robust M-estimate of GJR model with high frequency data
Acta Mathematicae Applicatae Sinica, English Series, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huang, Jin-shan +3 more
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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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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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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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Forecasting of Solar Power Volatility using GJR-GARCH method
2021 IEEE Electrical Power and Energy Conference (EPEC), 2021Sumana Ghosh, Pawan Kumar Gupta
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Short-Term Electricity Price Forecasting Using Hybrid SARIMA and GJR-GARCH Model
2017The liberalization of the power markets gained a remarkable momentum in the context of trading electricity as a commodity. With the upsurge in restructuring of the power markets, electricity price plays a dominant role in the current deregulated market scenario which is majorly influenced by the economics being governed. Electricity price has got great
Vipin Kumar +3 more
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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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