Volatility Jump Detection in Thailand Stock Market

Abstract

© 2018, Springer International Publishing AG, part of Springer Nature. The purposes of this study are threefold. The first is to employ three jump tests (Amed, Amin and BNS jump test) to detect jump in high-frequency return of the Stock Exchange of Thailand (SET) index over the period of five years from 2011 to 2016. The second is the application of the LLP test to detect jump in SET returns in respond to Thai macroeconomic news announcements using various GARCH-type models. The final purpose is to estimate the out-of-sample volatility forecasting and compare the results between GARCH-type models under various distributions using filtered and raw returns. This paper finds that (1) the jumps are significantly detected by Amed, Amin and BNS jump test in frequencies; (2) the number of jump detection in all samples are found between 1–3% of observations and the results also show that 1-h sample set and CGARCH models with Student’s t distribution have highest percentage of detected jump around 3%; (3) the simple GARCH-type models estimated using filtered return show more accurate out of sample forecasts of the conditional variance than GARCH estimated from raw return.