Asymmetric Garch Modelling Without Moment Conditions

Heavy tails and stability are two persistent challenges in modelling financial time series, yet most existing approaches rely on finite-moment assumptions and pay insufficient attention to stability issues.To bridge this gap, we propose an asymmetric GARCH model with standardized non-Gaussian stable innovations (sAGARCH), which accommodates infinite variance and even infinite mean.We establish a comprehensive inference framework for both stationary and explosive cases, proving the strong consistency and asymptotic normality of the maximum likelihood estimator, including the tail index parameter.We also discuss multiple estimators for the asymptotic variance.Additionally, we propose a modified Kolmogorov-type test statistic for diagnostic checking, along with tests for strict stationarity and asymmetry.Through Monte Carlo simulations with heavy-tailed innovations, we provide further insight into the finite-sample performance of the intercept estimator.Empirical applications to stock returns further highlight the usefulness and merits of the proposed sAGARCH model.