Abstract
One of the essential factors that lead to severe disruptions in financial markets is price bubbles and subsequent crashes. Numerous models for detecting bubbles have been developed, one of which (LPPLS) has lately attracted considerable interest. This study aims to utilize this model to detect price bubbles in Tehran Stock Exchange's index (TEDPIX). Confidence multi-scale indicators for this model are presented by fitting the LPPLS model to the data of the TSE index from 2009 through 2020. The bubble is detected when the number of fits that are in our filter conditions increases which means the growth of the indicator's value. By applying this method on TSE data two significant crashes in 2013 and 2020 are detected. The proposed technique can be useful for market participants to detect financial crashes and bubbles.
Highlights
The capital market is one of the most essential parts of the economy (Namaki, et al, 2021)
According to rational expectations theory, bubbles can be divided into rational and irrational ones (Diba & Grossman, 1982): A Rational bubble means that the current price of an asset is higher than its intrinsic value, but rational investors prefer to hold their assets or buy them if they do not possess the asset (Blanchard, 1979)
Market participants fall into two broad categories (Johansen, Ledoit, & Sornette, 2000): The first type is rational traders who mainly have the equivalent preferences and make decisions based on rational expectations (Sornette & Johansen, 1997)
Summary
The capital market is one of the most essential parts of the economy (Namaki, et al, 2021). Some Investors do not pay attention to intrinsic value and assume the price of an asset could consistently continue its growth (Johansen, 2003) They trade in a herding behavior approach and make irrational bubbles (Campbell et al, 1987). Garber observed hyperinflation in Germany during the First World War through an article on the subject: "Market Fundamentals versus Price-Level Bubbles: The First Tests.”. They concluded that by analyzing fundamental variables, bubbles cannot be detected. Sornette et al proposed a new method named log periodic power law singularity (LPPLS) that can detect bubbles By using this method, scholars have detected some major financial bubbles that prove the robustness of this model (Sornette, Johansen, and Bouchaud, 2000).
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