Bayesian Modelling of Time-Varying Conditional Heteroscedasticity
Bayesian Modelling of Time-Varying Conditional Heteroscedasticity
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DOI：
10.1214/21-ba1267
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发表时间：
2020-09
影响因子：
4.4
通讯作者：
Sayar Karmakar;Arkaprava Roy
中科院分区：
文献类型：
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作者：
Sayar Karmakar;Arkaprava Roy
条件异方差(CH)模型通常用于分析金融数据集。经典的具有时不变系数的ARCH-GARCH模型往往不足以描述由于市场的多变性而导致的频繁变化。然而，通过考虑这些模型的时变相似，我们可以获得更好的洞察力。本文提出了一种估计这类模型的贝叶斯方法，并发展了基于哈密顿蒙特卡罗(HMC)抽样的计算高效的MCMC算法。我们还根据平均Hellinger度量建立了随着样本量的增加而增加的后验收缩速率。将该方法的性能与频域估计法和时间常数类比估计法进行了比较。作为论文的结论，我们得到了一些流行的外汇(货币兑换率)和股票市场数据集的时变参数估计。
Conditional heteroscedastic (CH) models are routinely used to analyze financial datasets. The classical models such as ARCH-GARCH with time-invariant coefficients are often inadequate to describe frequent changes over time due to market variability. However we can achieve significantly better insight by considering the time-varying analogues of these models. In this paper, we propose a Bayesian approach to the estimation of such models and develop computationally efficient MCMC algorithm based on Hamiltonian Monte Carlo (HMC) sampling. We also established posterior contraction rates with increasing sample size in terms of the average Hellinger metric. The performance of our method is compared with frequentist estimates and estimates from the time constant analogues. To conclude the paper we obtain time-varying parameter estimates for some popular Forex (currency conversion rate) and stock market datasets.