全球金融海嘯以來，金融市場的穩健發展越來越引起學術界乃至國家政策制定者的關注，而金融泡沫的檢測則是其中不可或缺的一環。Evans在1991年提出了之後被廣為運用的間斷式破裂泡沫模型。從1988年Diba與Grossman開始用Bhargava檢定檢測股價膨脹以來，金融泡沫的研究致力尋找更加精確與通用的檢定方法。Homme與Breitung在2012年改進了多種統計量用於泡沫檢定，並研究對比了其表現的優劣。而Phillips和余俊等學者於2014年提出了利用遞歸的右尾單根檢定的Phillips, Yu and Shi (PSY)方法，其在實時監控泡沫方面具有出色的效力。在本文的研究過程中，我們提出了具有優秀性質的新泡沫模型與數據生成過程，克服了以往數據生成機制關於泡沫的不合理假定。本文最終致力於全面地對比分析在三種不同的泡沫生成過程之下，各類泡沫檢定統計方法的效力。

In light of the recent global financial crisis, the questions about financial bubbles and its detection has come back to the stage, attracting concerns from academics and policy makers. Evans(1991) created the famous periodically collapsing bubble model, which has been widely used as data generating process in previous bubble detection research. Since Diba and Grossman (1988) started the Bhargava Test for bubbles detection, the academic study of bubbles has gradually expanded to find more accurate and general method for detecting bubbles. Homm and Breitung (2012) put forward several modified statistics for bubble detection and compared their performance. Phillips, Yu et al.(2014) proposed the famous PSY method which has excellent performance in real-time bubble monitoring. During this research, we developed a new bubble model and two data generating processes which contain several meaningful properties and overcome the improper assumption of previous data generating mechanism. Then, our paper focus on comparing the performance of different bubble detection methods under three bubble data generating processes in a comprehensive view.

葉錦徽，林怡諄，朱珊瑩（2015）「拉高倒貨型股價操縱之台灣經驗與預警」, 《經濟論文》，43(4): 589–638。
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