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.

中文翻译：

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用非对称零漂移 GARCH 模型建模和预测金融资产的波动率

在这项研究中，我们扩展了零漂移广义自回归条件异方差 (GARCH) 模型，以纳入众所周知的冲击对金融波动性的不对称影响，并提出了一个不对称零漂移 GARCH (AZD-GARCH) 模型。结合仿真证据，对新模型的相关渐近性，包括拟最大似然估计量、稳定性检验和模型误规范检验的幂等进行了综合讨论。我们的实证研究侧重于 2019-2020 年最近期间的每日布伦特油价、澳元/美元汇率和标准普尔 500 指数回报。结果证明了 AZD-GARCH 模型在理解金融资产的波动性特征方面的有用性，以及该模型在精确预测波动性方面优于一系列竞争对手。对延长样本期（2017-2020 年和 2009-2020 年）数据的稳健性检查进一步提供了高度一致的结果。因此，提出的 AZD-GARCH 模型可以帮助政策制定者和市场参与者进行各种应用，例如监控资产波动性和对冲相关风险。