Journal of Data Science

Recent decades have witnessed a series of damages in the financial sector due to the unpleasant movements of prices beyond certain limits. These movements are commonly termed as Financial Bubbles. The formation and burst of a bubble creates huge damage in the field of finance. Hence in order to prevent the market from facing damages, the detection and modeling of financial bubble is very essential. We proposed improved test procedures for detecting financial bubbles by combining the existing Max test and Supremum Augmented Dickey Fuller (SADF) test generally used for detecting bubbles. The performance of proposed test is compared with existing tests via Monte Carlo simulation. It is observed that the proposed test have higher power compared to the existing tests, for detecting collapsible bubble irrespective of window length and collapsible probability. Further the power of proposed test increases as window size decreases. The empirical study of S&P 500 monthly data from January 2006 to December 2010 is carried out to demonstrate the advantages of proposed test procedures over existing tests.