Modeling and Forecasting Volatilities of Financial Assets with an Asymmetric Zero-Drift GARCH Model

Abstract

Abstract In this study, we extend the zero-drift generalized autoregressive conditional heteroskedasticity (GARCH) model to incorporate the well-known asymmetric effects of shocks on financial volatility and propose an asymmetric zero-drift GARCH (AZD-GARCH) model. Relevant asymptotics of the new model, including those for the quasi-maximum-likelihood estimator and the powers of the stability test and the model misspecification test, are comprehensively discussed with simulation evidence. Our empirical studies focus on the daily Brent oil price, the AUD/USD exchange rate, and the S&P 500 returns covering the recent 2019–2020 period. The results demonstrate the usefulness of the AZD-GARCH model in understanding the volatility features of financial assets and the model’s superiority to a range of competitors in precisely forecasting volatilities. Robustness checks on data for extended sample periods (2017–2020 and 2009–2020) further provide highly consistent results. Therefore, the proposed AZD-GARCH model can help policymakers and market participants in various applications, such as monitoring asset volatility and hedging relevant risks.