混合双自回归模型的贝叶斯推理

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica Pub Date : 2022-10-28 DOI:10.1111/stan.12281

Kai Yang, Qingqing Zhang, Xinyang Yu, Xiaogang Dong

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引用次数: 1

摘要

本文考虑了一种双分量混合双自回归模型，该模型能灵活地捕捉到多种金融收益通常表现出的异方差、大峰度和多模态边际等特征。采用基于现代马尔可夫链蒙特卡罗(MCMC)技术的贝叶斯方法估计模型参数。利用贝叶斯因子解决了底层过程的异方差检验问题。通过仿真对所提方法的性能进行了评价。结果表明，MCMC算法是处理混合模型的有效工具。最后，将该模型应用于标准普尔500指数数据集。

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Bayesian inference for a mixture double autoregressive model

This paper considers a mixture double autoregressive model with two components, which can flexibly capture the features usually exhibited by many financial returns such as heteroscedasticity, large kurtosis and multimodal marginals. Bayesian method based on modern Markov Chain Monte Carlo (MCMC) technology is used to estimate the model parameters. The heteroscedasticity test problem for the underlying process is also addressed by means of Bayes factor. The performances of the proposed methods are evaluated via some simulations. It is shown that the MCMC algorithm is an effective tool to deal with the mixture model. Finally, the proposed model is applied to the S&P500 index data.set.

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来源期刊

Statistica Neerlandica 数学-统计学与概率论

CiteScore

2.60

自引率

6.70%

发文量

审稿时长

>12 weeks

期刊介绍： Statistica Neerlandica has been the journal of the Netherlands Society for Statistics and Operations Research since 1946. It covers all areas of statistics, from theoretical to applied, with a special emphasis on mathematical statistics, statistics for the behavioural sciences and biostatistics. This wide scope is reflected by the expertise of the journal’s editors representing these areas. The diverse editorial board is committed to a fast and fair reviewing process, and will judge submissions on quality, correctness, relevance and originality. Statistica Neerlandica encourages transparency and reproducibility, and offers online resources to make data, code, simulation results and other additional materials publicly available.