论文标题
时变条件异质性的贝叶斯建模
Bayesian modelling of time-varying conditional heteroscedasticity
论文作者
论文摘要
有条件的异质机（CH）模型通常用于分析财务数据集。经典模型，例如带有时间不变系数的Arch-Garch通常不足以描述由于市场变异性而随着时间的流逝而频繁的变化。但是，通过考虑这些模型的时变类似物，我们可以实现更好的见识。在本文中，我们提出了一种贝叶斯方法来估计此类模型，并基于汉密尔顿蒙特卡洛（HMC）采样开发了计算有效的MCMC算法。我们还建立了后置收缩率，按照平均地狱指标的增加，样本量的增加。将我们的方法的性能与频繁估计和时间常数类似物的估计进行了比较。为了结束论文，我们获得了一些流行的外汇（货币转换率）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.
