递归均值调整模式下资产价格泡沫检验量分布与实证研究  

Distribution and Empirical Research of Asset Price Bubble Test under Recursive Mean Adjustment Mode

作　　者：江海峰 胡根华 JIANG Haifeng;HU Genhua(School of Business,Anhui University of Technology,Maanshan 243032,China)

机构地区：[1]安徽工业大学商学院,安徽马鞍山243032

出　　处：《安徽工业大学学报（自然科学版）》2022年第2期218-227,共10页Journal of Anhui University of Technology（Natural Science）

基　　金：安徽省自然科学基金项目(1908085MG227)。

摘　　要：为提高资产价格泡沫检验功效,在PWY检验量构造中引入递归均值调整方法,并讨论特定数据生成过程下检验量分布、临界值获取、检验水平和检验功效和实证研究。理论研究表明:检验量在大样本下收敛于维纳过程的泛函,但与已有检验量分布不同。蒙特卡洛模拟显示:递归均值调整检验量临界值与已有检验量临界值既具有共性,也具有异质性,且具有满意的检验水平;在样本容量较小和激增程度较弱时检验功效优势明显,在多种数据生成过程中检验功效全面占优。实证研究显示:递归均值调整检验量既能检验资产价格是否存在泡沫,也能识别泡沫发生时期。论文贡献在于,理论上提出一种资产价格泡沫检验新模型,实践上为实证研究提供一种新检验方法,因此丰富了资产价格泡沫检验理论。To improve the power of asset price bubble detection, the recursive mean adjustment method is applied to construct the PWY statistic and the distribution, critic values and size as well as power and empirical study of statistic under the specific data generation process are also analyzed. Theoretical research shows that the statistic converges to the functional of Wiener process in distribution under large sample, which is different from any of the existing statistic. Monte Carlo simulation shows that the critic values of recursive mean-adjusted statistic have both commonness and heterogeneity with that of existing statistic, as well as satisfactory size. When the sample size is small and the explosive degree is weak, the power is obviously superior. Specially, when the data is produced by several generation processes, the power is overall superior. The empirical research suggests that the recursive mean adjustment statistic can not only detect the existence of bubble in asset prices, but also identify the period when bubble occurs. The contribution of this paper is that it offers a new model for asset price bubble detection,and provides a new testing method for empirical research, which enriches the theory of asset price bubble detection.

关 键 词：递归均值调整 资产价格泡沫 SADF检验量 蒙特卡洛模拟 

分 类 号：F224.0[经济管理—国民经济] O212.1[理学—概率论与数理统计]