Results 171 to 180 of about 11,660 (201)

Value at Risk Prediction for the GJR-GARCH Aggregation Model

Pattimura International Journal of Mathematics (PIJMath), 2022
Volatility 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, Wiwin Apriani, Nurhayati Nurhayati   +2 more
openaire   +1 more source

Quantum prediction GJR model and its applications

Statistica Neerlandica, 2014
In 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
openaire   +1 more source

Semiparametric efficient adaptive estimation of the GJR-GARCH model

Statistics and Risk Modeling, 2018
Abstract In this paper we derive a semiparametric efficient adaptive estimator for the GJR-GARCH ( 1 , 1 )
exaly   +3 more sources

GJR-GARCH process with normal errors of varying mean

Communications in Statistics Part B: Simulation and Computation
Yakoub Boularouk
exaly   +2 more sources

GJR-GARCH model in value-at-risk of financial holdings

Applied Financial Economics, 2011
In 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
exaly   +2 more sources

Approximating the GJR-GARCH and EGARCH option pricing models analytically

The Journal of Computational Finance, 2006
In 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, Geneviève Gauthier, Jean-Guy Simonato, Caroline Sasseville   +3 more
openaire   +1 more source

Building Fuzzy Levy-GJR-GARCH American Option Pricing Model

2019
Taking 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
openaire   +1 more source

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
Yi-Hsien Wang
exaly   +2 more sources

Study on Dynamic Risk Measurement Based on ARMA-GJR-AL Model [PDF]

Applied and Computational Mathematics, 2015
This paper established the ARMA-GJR-AL model of dynamic risk VaR and CVaR measurement. Considering from aspects of the correlation and volatility and residual distribution characteristics, studying the dynamic risk measures of VaR and CVaR based on ARMA-GJR-AL model.
exaly   +2 more sources

Forecasting of Solar Power Volatility using GJR-GARCH method

2021 IEEE Electrical Power and Energy Conference (EPEC), 2021
Sumana Ghosh, Pawan Kumar Gupta
openaire   +1 more source