The impact of COVID-19 on volatility spillover in Stock Exchange of Thailand

This research focuses on the volatility spillover between the returns of the Stock Exchange of Thailand (SET) index and the return of the SET index in each industry which has grouped the companies into 8 industries. The search for COVID-19 words in Thailand was retrieved from the Google Trends database as an exogenous variable. Our focus has been on four periods: the first started on 2 January 2020 and ended on 1 June 2020, the second started on 2 November 2020 and ended on 1 March 2021, the third started on 1 March 2021 and ended on 15 June 2021, and the last started on 16 June 2021 and ended on 1 November 2021. In this study, a multivariate generalized autoregressive conditional heteroscedasticity Diagonal BEKK model (DBEKK) with an exogenous variable is applied to measure spillovers in covolatility. According to this study, the autoregressive conditional heteroscedasticity (ARCH) effect was observed in the first and third periods. In the first period, the ARCH effect was observed on the return of the SET index, as well as the return of four industries, including consumer products, financials, property construction, and technology, while in the third period we noticed the ARCH effect on the following return of industries: the resources, services, and technology. Note that neither the second nor the fourth period showed an ARCH effect. According to Google Trends for COVID-19 searches in Thailand, the financials index had a higher level of shocks and sensitivity in the first period. Additionally, the SET index demonstrated high levels of volatility transmission and was highly correlated with other industries, particularly construction, financials, and technology. The technology industry was more sensitive to shocks in the third period. However, the level of volatility transmission was higher in the services industry. This indicated that the volatility of the services industry was highly correlated with that of other industries.