基于 i.i.d. 数据的多元 Student-t 分布和 GARCH 观察结果的拟合优度检验

IF 1.2 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Time Series Analysis Pub Date : 2023-08-03 DOI:10.1111/jtsa.12713

Simos Meintanis, Bojana Milošević, Marko Obradović, Mirjana Veljović

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引用次数: 0

摘要

我们考虑用 i.i.d. 数据对多元 Student's t 分布进行拟合优度检验，以及对广义自回归条件异方差模型中的创新分布进行拟合优度检验。这些方法以经验特征函数为基础，相对容易实现，在线性变换下不变，并且全局一致。研究了所提程序的渐近特性，并通过蒙特卡罗研究说明了其有限样本特性。这些程序还应用于金融市场的真实数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations

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Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations

We consider goodness-of-fit tests for the multivariate Student's t-distribution with i.i.d. data and for the innovation distribution in a generalized autoregressive conditional heteroskedasticity model. The methods are based on the empirical characteristic function and are relatively easy to implement, invariant under linear transformations, and globally consistent. Asymptotic properties of the proposed procedures are investigated, while the finite-sample properties are illustrated by means of a Monte Carlo study. The procedures are also applied to real data from the financial markets.

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来源期刊

Journal of Time Series Analysis 数学-数学跨学科应用

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.