FORECASTING THE VOLATILITY OF LEADING BANKING INSTITUTIONS USING THE EGARCH (1.1) MODEL

Authors

Keywords
volatility, EGARCH model, market risk, conditional variance, financial time series, banking sector, volatility clustering, asymmetric effects

This study focuses on the analysis and forecasting of stock price volatility among leading banking institutions using the EGARCH (1,1) model. In a dynamic and often volatile financial environment, the accurate modeling of market risk is of critical importance to both investors and financial institution management. The EGARCH model is particularly suitable for capturing asymmetric effects in volatility, whereby negative market shocks ex¬ert a stronger impact on volatility than positive shocks of comparable magnitude.

The study examines twenty of the largest banks worldwide selected based on their market capitalization and analyzes the dynamics of their realized and conditional volatility over the period 2020–2025. The results indicate that the EGARCH (1,1) model effectively captures the key characteristics of the volatility process, including volatility clustering and the persistence of risk, although it exhibits a tendency to smooth extreme observations. Overall, the findings suggest that the EGARCH (1,1) model constitutes a robust and reliable tool for analyzing and forecasting market volatility in the context of the global banking sector.

JEL: G17, C22, G21
Pages: 25
Price: 2 Points

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