GB2和偏态广义log-t分布在金融中的应用比较

IF 9.9 3区经济学 Q1 ECONOMICS

Journal of Econometrics Pub Date : 2024-03-01 DOI:10.1016/j.jeconom.2021.01.003

Joshua D. Higbee , James B. McDonald

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引用次数: 0

摘要

有多个统计分布系列被用于建立金融数据模型。四参数第二类广义贝塔分布（GB2）和五参数偏斜广义对数 t 分布（SGT）已分别用于拟合收益率和对数收益率数据。我们介绍了偏斜广义对数 t（SGLT）分布，并指出 GB2 和 SGLT 与非对称对数拉普拉斯（ALL）、对数拉普拉斯（LL）和对数正态（LN）等分布相同。然后，我们比较了 GB2 和 SGLT 在模拟日、周和月股票收益率数据分布时的相对表现。我们发现，GB2 和 SGLT 的表现类似，而三参数 log-t (LT) 分布则相当稳健。

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A comparison of the GB2 and skewed generalized log-t distributions with an application in finance

Several families of statistical distributions have been used to model financial data. The four-parameter generalized beta of the second kind (GB2) and five-parameter skewed generalized t (SGT) have been fit to return and log-return data, respectively. We introduce the skewed generalized log-t (SGLT) distribution and note that the GB2 and SGLT share such distributions as the asymmetric log-Laplace (ALL), log-Laplace (LL), and log-normal (LN). We then compare the relative performance of the GB2 and SGLT in modeling the distribution of daily, weekly, and monthly stock return data. We find that the GB2 and SGLT perform similarly and that the three-parameter log-t (LT) distribution is quite robust.

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来源期刊

Journal of Econometrics 社会科学-数学跨学科应用

CiteScore

8.60

自引率

1.60%

发文量

220

审稿时长

3-8 weeks

期刊介绍： The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.