Crisis Monitoring in Financial Sectors Using CAMEL Partial Triadic Analysis Model

Abstract

This study presents the CAMEL Partial Triadic Analysis (CPTA) model, which combines CAMEL methodology with Partial Triadic Analysis (PTA) to evaluate financial indicators on a quarterly basis and delineate risk levels. By analyzing symmetries in data matrices and quantifying vector correlations, the model provides detailed insight into financial trends during recessionary periods, which is useful for financial regulators, public policy makers, and private banking institutions. The CPTA incorporates the commitment matrix, which synthesizes indicator values and provides stability in data matrices in adverse environments. Furthermore, the Fibonacci retracements technique was utilized to categorize variables, identify upward and downward trends, and estimate the solvency of each bank in relation to its respective segment. These results are contrasted using the HJ-Biplot and the dynamic HJ-Biplot, which allow for the evaluation of banking scenarios in the context of financial turbulence. When applied to the Ecuadorian banking sector, the model identified significant fluctuations in three key periods: pre-pandemic, pandemic, and the territorial crisis between Russia and Ukraine. Future research could extend the model by integrating all balance sheet variables against the base CPTA model through the application of Hidden Markov models.