A Random-Matrix-Theory-Based Analysis of the Brazilian Stock Market During the 2008 Financial Crisis and Asian Crisis and Temporal Neighborhoods

Abstract

Several approaches and concepts of physics, such as Random Matrix Theory, have been used to investigate the complexity of financial time series. This study aims to analyze the spectrum of stock correlation in the Brazilian stock market by applying Random Matrix Theory to the subprime and Asian financial crisis periods and their temporal neighborhoods. Results show evident synchronized market behavior during both crises. The results also show that the period preceding a crisis presents symptoms which may predict future crises. Thereby, the methodology presented here could be used by the market as a tool that helps to anticipate possible market fluctuations.

Research on Chinese Stock Market during COVID-19—Based on Random Matrix Theory

“…Te cross-correlation matrix between several stocks, which has unexpected properties due to complex behaviors, such as mispricing, bubbles, market crashes, and so on, is an important parameter to understand the interactions in the fnancial market. To analyze the actual cross-correlation matrix, the random matrix theory (RMT) proposed by Wigner is a useful and technical tool for eliminating the randomness in the actual cross-correlation matrix [13][14][15][16]. Laloux et al selected 406 stocks in S&P500 and calculated the correlation coefcient matrix for a total of 1309 days from 1991 to 1996, which found that about 94% of the eigenvalues fell within the prediction range and less than 6% of the eigenvalues fell outside the prediction range [17].…”