This paper revisits the topic of time-scale parameterizations of the Heston-Nandi GARCH (1,1) model to create a new, theoretically valid setting compatible with real financial data. We first estimate parameters using three US market indices and six frequencies to let data reveal the correct,...

We construct a Generalized Empirical Likelihood estimator for a GARCH(1,1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions...

This paper proposes an adaptive quasi-maximum likelihood estimation when forecasting the volatility of financial data with the generalized autoregressive conditional heteroscedasticity(GARCH) model. When the distribution of volatility data is unspecified or heavy-tailed, we worked out adaptive...