Results 171 to 180 of about 36,906 (198)

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 J Lin
exaly   +2 more sources

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

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   +3 more sources

Hedging Agriculture Commodities Futures with Histogram Data Based on Conditional Copula-GJR-GARCH

Lecture Notes in Computer Science, 2022
Pichayakone Rakpho
exaly   +3 more sources

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
exaly   +2 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

Use of GJR-GARCH Model for Forecasting of Financial Risk

Lecture Notes in Networks and Systems
Vivek Vijay
exaly   +2 more sources

A new GJR‐GARCH model for ℤ‐valued time series

Journal of Time Series Analysis, 2021
The 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
openaire   +1 more source

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