论文标题
在财务数据波动动态中检测变更点的半参数方法
A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data
论文作者
论文摘要
财务时间序列数据的最重要特征之一是波动性。随着时间的推移，波动率通常会发生结构性变化，并且对财务时间序列的波动性的准确估计需要仔细识别变更点。建模时间序列数据波动率的一种常见方法是众所周知的GARCH模型。尽管文献中已经考虑了从GARCH模型得出的波动率动态变化点估计的问题，但这些方法依赖于条件误差分布的参数假设，这些假设通常在财务时间序列中违反。这可能导致变更点检测的不准确性，导致GARCH波动性估计不可靠。本文介绍了一种基于半参数GARCH模型的新型更改点检测算法。所提出的方法保留了GARCH过程的结构优势，同时结合了非参数条件误差分布的灵活性。该方法利用了源自半参数GARCH模型和有效的二进制分割算法的惩罚可能性。结果表明，就更改点的估计和检测精度而言，半参数方法在广泛的方案中优于常用的准摩尔（QMLE）和GARCH模型的其他变体。
One of the most important features of financial time series data is volatility. There are often structural changes in volatility over time, and an accurate estimation of the volatility of financial time series requires careful identification of change-points. A common approach to modeling the volatility of time series data is the well-known GARCH model. Although the problem of change-point estimation of volatility dynamics derived from the GARCH model has been considered in the literature, these approaches rely on parametric assumptions of the conditional error distribution, which are often violated in financial time series. This may lead to inaccuracies in change-point detection resulting in unreliable GARCH volatility estimates. This paper introduces a novel change-point detection algorithm based on a semiparametric GARCH model. The proposed method retains the structural advantages of the GARCH process while incorporating the flexibility of nonparametric conditional error distribution. The approach utilizes a penalized likelihood derived from a semiparametric GARCH model and an efficient binary segmentation algorithm. The results show that in terms of change-point estimation and detection accuracy, the semiparametric method outperforms the commonly used Quasi-MLE (QMLE) and other variations of GARCH models in wide-ranging scenarios.
