TY - JOUR
T1 - Forecasting the extreme impact of Covid-19 on airline and petroleum stocks
T2 - a comparison of alternative time-series models
AU - Khan, Mushtaq Hussain
AU - Feroze, Navid
AU - Ahmed, Junaid
AU - Mughal, Mahzar
N1 - Publisher Copyright:
© 2024, Emerald Publishing Limited.
PY - 2025/1/2
Y1 - 2025/1/2
N2 - Purpose: Earlier studies used conventional time-series models to forecast the impact of the Covid-19 pandemic on stock market performance. This study aims to provide a more flexible model that offers more robust estimation features, such as incorporating additional information (prior) about the model parameters, capturing the evolving behavior of the parameters over time and being able to include several covariates using a spike and slab prior, within the context of the Covid-19 shock and its effect on stock market performance. Design/methodology/approach: Empirically, this paper compares autoregressive integrated moving average (ARIMA) models and the proposed Bayesian structural time-series (BSTS) models regarding their forecasting accuracy for airline and petroleum stocks in the five countries most affected by the Coronavirus, namely, Brazil, France, India, Russia and the USA. In addition, the authors estimate the difference between the pre- and post-intervention periods of the observed series of stock prices and a simulated time-series that would have occurred without the extreme event of Covid-19, using intervention analysis under the best-performing models. Findings: The forecasting results, based on the trend, seasonality and regression components, demonstrate that BSTS models respond faster to the diverse needs of time-series analysis in unprecedented and crisis conditions compared to ARIMA models. Therefore, the authors use intervention analysis under BSTS models to examine the impact of Covid-19 intervention on stock market performance. The authors find that the Covid-19 shock had an adverse effect on the stock markets of the selected countries. The impact was more pronounced in the Brazilian market, where the average weekly prices of airline and petroleum stocks plummeted by 76% and 29%, respectively. Originality/value: To the best of the authors’ knowledge, no prior study has carried out intervention analysis under BSTS models to forecast the impact of Covid-19 intervention on stock market returns. This study attempts to fill this methodological gap in the literature.
AB - Purpose: Earlier studies used conventional time-series models to forecast the impact of the Covid-19 pandemic on stock market performance. This study aims to provide a more flexible model that offers more robust estimation features, such as incorporating additional information (prior) about the model parameters, capturing the evolving behavior of the parameters over time and being able to include several covariates using a spike and slab prior, within the context of the Covid-19 shock and its effect on stock market performance. Design/methodology/approach: Empirically, this paper compares autoregressive integrated moving average (ARIMA) models and the proposed Bayesian structural time-series (BSTS) models regarding their forecasting accuracy for airline and petroleum stocks in the five countries most affected by the Coronavirus, namely, Brazil, France, India, Russia and the USA. In addition, the authors estimate the difference between the pre- and post-intervention periods of the observed series of stock prices and a simulated time-series that would have occurred without the extreme event of Covid-19, using intervention analysis under the best-performing models. Findings: The forecasting results, based on the trend, seasonality and regression components, demonstrate that BSTS models respond faster to the diverse needs of time-series analysis in unprecedented and crisis conditions compared to ARIMA models. Therefore, the authors use intervention analysis under BSTS models to examine the impact of Covid-19 intervention on stock market performance. The authors find that the Covid-19 shock had an adverse effect on the stock markets of the selected countries. The impact was more pronounced in the Brazilian market, where the average weekly prices of airline and petroleum stocks plummeted by 76% and 29%, respectively. Originality/value: To the best of the authors’ knowledge, no prior study has carried out intervention analysis under BSTS models to forecast the impact of Covid-19 intervention on stock market returns. This study attempts to fill this methodological gap in the literature.
KW - ARIMA models
KW - Bayesian structural time-series models
KW - Covid-19
KW - Extreme event
KW - Stock prices
UR - http://www.scopus.com/inward/record.url?scp=85214106416&partnerID=8YFLogxK
U2 - 10.1108/JM2-04-2023-0071
DO - 10.1108/JM2-04-2023-0071
M3 - Article
AN - SCOPUS:85214106416
SN - 1746-5664
JO - Journal of Modelling in Management
JF - Journal of Modelling in Management
ER -