Prediction Model Estimation and Dynamic Characteristics Analysis of Exchange Rate and KOSDAQ Index
Keywords:VAR, VECM, Granger causality test, Cointegration test, Impulse response function
In this study, a predictive model based on vector autoregressive model (VAR) was estimated using monthly multivariate time series data of exchange rate (ER) and KOSDAQ index (KOSDAQ), and dynamic characteristics analysis of ER and KOSDAQ using impulse response function was carried out. To this end, the ADF unit root test was performed to confirm the stability of the data, the linear dependence relationship between variables was examined by the Granger causality test, and the existence of a constant term was confirmed by the t-statistic. The VAR model was identified using the AICC statistic of the minimum information criterion. In order to avoid spurious regression of the identified model, the cointegration test was performed for the cases that the error correction term had a constant intercept and that the VECM(p) term had a constant intercept and no linear trend. And it was found that the cointegration coefficient did not exist. Therefore, the prediction model of ER and the KOSDAQ could be estimated by the VAR model. As a result of the prediction, ER and KOSDAQ were predicted to remain stable after about 3 months. And the dynamic response in the forecasting model was evaluated using the impulse response function. As a result of the analysis, it was analyzed that when the shock of ER occurred, the effect of the shock disappeared after about 8 months in KOSDAQ, and that when the shock of KOSDAQ occurred, the effect of the shock disappeared after about 5 months in ER.
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