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Analytic moments for GJR-GARCH (1, 1) processes
International Journal of Forecasting, 2021 For a GJR-GARCH(1,1) specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moments for the most commonly used GARCH models are stated as special cases.
Carol Alexander, Emese Lazar
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GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies [PDF]
Journal of Risk and Financial Management, 2021 Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market
Fahad Mostafa +2 more
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Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model
Procedia Computer Science, 2014 AbstractVolatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction.
David Enke
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A new GJR‐GARCH model for ℤ‐valued time series
Journal of Time Series Analysis, 2021The Glosten–Jagannathan–Runkle GARCH (GJR‐GARCH) model is popular in accounting for asymmetric responses in the volatility in the analysis of continuous‐valued financial time series, but asymmetric responses in the volatility are also observed in time series of counts or ‐valued time series, such as the daily number of stock transactions or the daily ...
Yue Xu, Fukang Zhu
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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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Semiparametric efficient adaptive estimation of the GJR-GARCH model
Statistics and 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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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
Yi-Hsien Wang
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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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Measuring extreme risk of sustainable financial system using GJR-GARCH model trading data-based
International Journal of Information Management, 2020Abstract This paper investigates the role of gold as a safe haven for stock markets and the US dollar by examining the extreme risk spillovers. The extreme risk is measured by Value at Risk (VaR), which is estimated by GJR-GARCH model based on skewed t distribution.
Zou Dong, Rui Liu
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