Informational efficiency and modeling of international financial markets

Abstract

This study aimed to model the informational efficiency and volatility dynamics of international financial markets by analyzing the daily returns of 13 major stock indices (e.g., NASDAQ, Euro Stoxx 50, FTSE 100) from 2017 to 2025, narrowing the sample to five least-correlated indices for diversification. Using econometric tools such as ARMA, ARCH, GARCH, EGARCH, and TARCH models, alongside unit root tests (ADF, PP, KPSS) and volatility clustering diagnostics, the study confirmed market efficiency through a White Noise process in mean returns, while identifying significant volatility clustering, asymmetry, and leptokurtosis. Results showed that EGARCH(1,1,1) models best captured asymmetric volatility effects for most indices, with forecasts projecting the NASDAQ Composite to yield the highest return (14.06%) and risk (1.62%) by June 2025.