独立数据和条件异方差数据的多变量稳定-Paretian定律的规范程序

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY Test Pub Date : 2023-12-15 DOI:10.1007/s11749-023-00909-3

Simos G. Meintanis, John P. Nolan, Charl Pretorius

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引用次数: 0

摘要

我们考虑了任意维度的多元对称和非对称稳定帕累托随机向量的拟合优度方法。这些方法以经验特征函数为基础，在 i.i.d. 情况下以及针对 GARCH 模型中的创新时均可实施。我们讨论了所建议程序的渐近特性，并通过大量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。

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Specification procedures for multivariate stable-Paretian laws for independent and for conditionally heteroskedastic data

We consider goodness-of-fit methods for multivariate symmetric and asymmetric stable Paretian random vectors in arbitrary dimension. The methods are based on the empirical characteristic function and are implemented both in the i.i.d. context as well as for innovations in GARCH models. Asymptotic properties of the proposed procedures are discussed, while the finite-sample properties are illustrated by means of an extensive Monte Carlo study. The procedures are also applied to real data from the financial markets.

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来源期刊

Test 数学-统计学与概率论

CiteScore

2.20

自引率

7.70%

发文量

审稿时长

>12 weeks

期刊介绍： TEST is an international journal of Statistics and Probability, sponsored by the Spanish Society of Statistics and Operations Research. English is the official language of the journal. The emphasis of TEST is placed on papers containing original theoretical contributions of direct or potential value in applications. In this respect, the methodological contents are considered to be crucial for the papers published in TEST, but the practical implications of the methodological aspects are also relevant. Original sound manuscripts on either well-established or emerging areas in the scope of the journal are welcome. One volume is published annually in four issues. In addition to the regular contributions, each issue of TEST contains an invited paper from a world-wide recognized outstanding statistician on an up-to-date challenging topic, including discussions.