A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Zeynab Aghabazaz, I. Kazemi, A. Nematollahi
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引用次数: 2

Abstract

This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence.
一种时变GARCH混合效应模型,用于隔离高、低频波动和共波动
本文通过将建模策略引入到多元数据分析中来研究股票市场的长期、短期波动性和共同波动性,该分析处理了序列相关的创新和横截面依赖性。特别是,它通过GARCH过程提出了一个创新的混合效应模型,考虑了异质性效应和时间序列动力学。我们提出了一个惩罚低秩平滑样条的非参数回归模型,将时间趋势表示为方差和协方差方程。该策略为股票市场的低频波动率和共同波动率提供了灵活的建模。使用改进的Cholesky因子分解对分解后的低频矩阵进行建模。哈密尔顿蒙特卡罗技术被实现为用于估计参数和潜在因素的贝叶斯计算过程。我们的建模策略在实证研究中的优势通过研究潜在金融因素对110周系列10只股票的影响而得到强调。该模型可以区分方差-协方差结构方程的高频和低频的非参数动态模式,并结合经济特征来预测股票市场关于时间序列证据的可变性。
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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来源期刊
Statistical Modelling
Statistical Modelling 数学-统计学与概率论
CiteScore
2.20
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: The primary aim of the journal is to publish original and high-quality articles that recognize statistical modelling as the general framework for the application of statistical ideas. Submissions must reflect important developments, extensions, and applications in statistical modelling. The journal also encourages submissions that describe scientifically interesting, complex or novel statistical modelling aspects from a wide diversity of disciplines, and submissions that embrace the diversity of applied statistical modelling.
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