SARS和COVID-19期间股票收益波动:马来西亚和中国股票市场的比较

S. R. Hamzah, Azah Syafinaz Azhar, N. Ishak, Ahmad Fadly Nurullah Raseedee
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摘要

本研究分析了SARS和COVID-19大流行期间马来西亚和中国股市的回报波动性。使用GARCH族模型(GARCH (1,1), TGARCH和EGARCH)估计股票收益波动率。一般来说,GARCH(1,1)估计对称条件,而TGARCH和EGARCH估计不对称条件或收益波动的杠杆效应。然后比较中国和马来西亚的股票回报波动性,以评估研究期间大流行病例的严重程度。大流行后,马来西亚在使用TGARCH模型时,杠杆效应下降幅度较大。相反,当使用EGARCH模型预测SARS大流行时,对中国的影响更大。为了预测新冠肺炎大流行后的未来收益波动率,对SARS大流行后的收益波动率进行了预测,并利用平均绝对误差(MAE)、均方根误差(RMSE)和泰尔不等式系数(TIC)方法,将预测值作为误差评估的依据。对预测误差表现进行排序,以确定在大流行期间表现较好的GARCH家族模型:中国股市的TGARCH模型和马来西亚股市的GARCH模型。©2022作者。
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Stock return volatility during SARS and COVID-19: Comparison between Malaysia and China's stock markets
This study analyses return volatility for Malaysia and China's stock markets during the SARS and COVID-19 pandemics. Stock return volatility is estimated using GARCH family models (GARCH (1,1), TGARCH and EGARCH). Generally, GARCH (1,1) estimates symmetric conditions, whereas TGARCH and EGARCH estimate the asymmetric condition or leverage effect of return volatility. Stock return volatility in China and Malaysia are then compared to assess the severity of pandemic cases during the study period. Post pandemic, Malaysia is seen to experience higher decrements in leverage effect when using the TGARCH model. Conversely, the effect is higher for China when using the EGARCH model for the SARS pandemic. To aid in predicting future return volatility after the COVID-19 pandemic, return volatility after the SARS pandemic is forecast, with the forecast value serving as the basis for evaluating the error using the mean absolute error (MAE), root mean square error (RMSE) and Theil inequality coefficient (TIC) approaches. The forecast error performance is ranked to identify outperforming GARCH family models for the pandemic period: the TGARCH model for China's stock market but the GARCH model for Malaysia's stock market. © 2022 Author(s).
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