2017
DOI: 10.1080/23322039.2017.1355503
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A simulation study on the distributions of disturbances in the GARCH model

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Cited by 20 publications
(27 citation statements)
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“…Traditionally, the Student's t-distribution and general error distribution are used alternatively to solve this problem. [38][39][40][41] The asymmetry* was positive, meaning that the result of the kurtosis statistic, which measures the peak or flatness of the distribution, exceeds 3 (normal value), indicating a distribution with a caudal flattening. 42,43 After the initial descriptive analysis, the next step is to perform the unit root test.…”
Section: Methodsmentioning
confidence: 99%
“…Traditionally, the Student's t-distribution and general error distribution are used alternatively to solve this problem. [38][39][40][41] The asymmetry* was positive, meaning that the result of the kurtosis statistic, which measures the peak or flatness of the distribution, exceeds 3 (normal value), indicating a distribution with a caudal flattening. 42,43 After the initial descriptive analysis, the next step is to perform the unit root test.…”
Section: Methodsmentioning
confidence: 99%
“…It was found that EGARCH adequately captured the volatile nature of the innovations, in line with Mandimika & Chinzara (2012), Ilupeju (2016). However, the model failed to capture asymmetry and nonnormality, in line with Mangani (2008), Ilupeju (2016), Feng & Shi (2017). The risk-return relationship was investigated by the GARCH-M and EGARCH-M.…”
Section: Discussionmentioning
confidence: 81%
“…Jin (2017) highlighted that the GARCH approach is prone to model misspecification as it has underlying assumptions and constraints due to being a parametric model. Furthermore, Feng & Shi (2017) found that risk remained uncaptured by the GARCH model's innovations. Khan et al (2016) recommended to analyze economic fundamentals as these were driving forces of asymmetric volatility.…”
Section: Literature Reviewmentioning
confidence: 99%
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“…Originally, the GARCH model was specified based on the normal distribution for the innovations yet could not capture the heavy-tailed characterizations. Similarly, the student-t distribution which is traditionally specified to remedy the weakness of the normal distribution in accommodating the heavy-tailed property, is found wanting in many applications to account for excess kurtosis and thus, the resulting estimates of GARCH models are not efficient (Moffat and Akpan [3]; Feng and Shi [4]).…”
Section: Introductionmentioning
confidence: 99%