IT산업과 종합주가지수 사이의 정보메커니즘에 관한 연구

This paper studies the information spillover effects between IT industry index and KOSPI using the daily data from March, 2015 to March, 2020 with VAR models and GARCH(1,1)-M model, GJR-GARCH(1,1)-M model. The Granger causality test showed mutual feedback predictive power between the change in the IT industry index rate of return and the change in the KOSPI rate of return, but the probability of rejecting the change in the KOSPI rate of return as the change in the IT industry index rate of return was high at the level of statistical significance. The results of the GARCH(1,1)-M model using heteroskedasticity also showed results consistent with the results of the VAR model, showing that there is mutual predictive power between changes in IT industry index returns and changes in KOSPI returns. Lastly, the past information was decomposed and analyzed through the GJR-GARCH(1,1)-M model, an asymmetric model, to determine whether the information transfer profit came from good news information or bad news information. As a result of the analysis, it was found that in the case of changes in the IT industry index rate of return, only adverse news information affected the change in the KOSPI rate of return. On the other hand, in the case of changes in KOSPI returns, it was found that in addition to bad news information, good news information also affected the change in IT industry index returns at the level of statistical significance. These results are expected to provide investors with useful information when managing portfolios or selecting index investments in practice. In addition, while existing analyzes in academia mainly focused on research on the information transmission mechanism for stock index currents/futures from a macro aspect, it was now suggested that there was a need to identify the information transmission mechanism using sector-specific data from a micro aspect.