Do commodity assets hedge uncertainties? What we learn from the recent turbulence period? [PDF]
Hasan MB +4 more
europepmc +1 more source
GJR-GARCH Model in Value-at-Risk of Financial Holdings
近年來金融環境變化迅速,由於新金融商品的開放及金融機構間激烈的競爭,金融機構無不增加其交易活動以改善其獲利,隨之也帶來顯著的市場風險。市場風險值(Value-at-Risk,VaR)目前已經成為衡量金融機構市場風險的標準方法。 本研究導入一不對稱GARCH模型-GJR-GARCH模型,來計算市場風險值,探討使用GJR-GARCH模型是否可以更精確地衡量金融機構的市場風險。由於缺乏實際的每日交易損益資料,我們模擬兩個投資組合A和B,分別代表富邦和國泰世華金融控股公司 ...
Lin, Yun-Ju, 林韻茹
core
COVID-19 and stock returns: Evidence from the Markov switching dependence approach. [PDF]
Bouteska A, Sharif T, Abedin MZ.
europepmc +1 more source
Bayesian estimation of the Gaussian mixture AR-GJR-GARCH model with Griddy-Gibbs sampler
Considering the characteristics of the volatility such as excess kurtosis and leverage effect, the authors propose a Gaussian mixture AR-GJR-GARCH model. The parameters of the model are estimated by using MCMC method based on Griddy-Gibbs sampler.
ZHANG Xin-Xing, TANG Ya-Yong
doaj
Is Tether a safe haven of safe haven amid COVID-19? An assessment against Bitcoin and oil using improved measures of risk. [PDF]
Syuhada K +4 more
europepmc +1 more source
A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model. [PDF]
Song M, Sui Z, Zhao X.
europepmc +1 more source
The linkage between Bitcoin and foreign exchanges in developed and emerging markets. [PDF]
BenSaïda A.
europepmc +1 more source
Applying Copula-GJR-GARCH Model in the Hedging of Gold Futures and Silver Futures
金融資產報酬通常為厚尾且非常態,Copula 函數能夠依據個別資料之間的關聯性找出最適之聯合分配,使得模型的運用上更加具有彈性。本文以傳統OLS避險模型為指標、並考慮固定條件相關(CCC-GJR-GARCH)避險模型、動態條件相關(DCC-GJR-GARCH)避險模型以及以Copula-based GJR-GARCH 避險模型。實證上利用最小變異避險理論求出避險比例並衡量避險績效。比較不同避險模型的樣本內、外績效,發現以Copula 為基礎的GJR-GARCH 模型能夠提供較佳的避險 ...
李沃牆; 李莠苓
core
Multimodality in the GARCH Regression Model [PDF]
Several aspects of GARCH(p,q) models that are relevant for empirical applications are investigated. In particular, it is noted that the inclusion of dummy variables as regressors can lead to multimodality in the GARCH likelihood.
Jurgen A. Doornik, Marius Ooms
core
Forecasting with a Bivariate Hysteretic Time Series Model Incorporating Asymmetric Volatility and Dynamic Correlations. [PDF]
Than HT.
europepmc +1 more source

