Kaiji Gong Humanities and Social Science
The Extension of the Log Periodic Power Law: How to Predict After the Crash
To judge whether an economic bubble would lead to a financial crash and to estimate the critical time of a crash are significant in financial areas. The Log-Periodic Power Law (LPPL) is an equation that describes how bubbles evolve and grow. By fitting the equation into a financial time series, it is possible to predict the event of a crash. The equation proves to be effective in predicting several financial crises, such as the one in 2008. My research will focus on extending the model to predict market behaviors after the crash. I will adopt some assumptions in LPPL (i.e., imitation behaviors among traders) as well as some more theories from other literatures, and follow the similar track to construct a model that predicts when the market will stop crash. I will then fit the model into financial time series to examine the accuracy of the model.