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. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal … react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting …
Persistent link: https://www.econbiz.de/10014419406
/methodology/approach - The copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market … pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models …
Persistent link: https://www.econbiz.de/10014445508
validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error …
Persistent link: https://www.econbiz.de/10011489480
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The unprecedented global pandemic of COVID-19 has greatly impacted the stock market in terms of both price reactions and the influences of volatility. Using a sample of 46 stocks listed in the Stock Exchange of Thailand, in this paper, an event study technique is developed considering...
Persistent link: https://www.econbiz.de/10012311822
nonlinear volatility models (symmetric and asymmetric Generalized AutoRegressive Conditional Heteroskedasticity [GARCH …
Persistent link: https://www.econbiz.de/10012131511
exponential-GARCH(1,1) and GJR-GARCH(1,1) incorporating the market reactions to news. We noted the returns distribution is skewed … model the behaviour of the tails and capture the asymmetry in the distribution of the returns. The GJR-GARCH(1,1) with the …
Persistent link: https://www.econbiz.de/10011843965
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Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which...
Persistent link: https://www.econbiz.de/10012629835