Category - Mei-Yu LEE

Yuanpei University, Taiwan

Ya-Chuan CHAN Yao-Hsin LEE Mei-Yu LEE

Does COVID-19 Change the Relationship among Taiwan Stock Index Futures, MSCI Morgan Taiwan Index, and Taiwan Stock Price Index?

This paper discusses the changes of Taiwan stock index futures’(TF) return rate before and during COVID-19, using the nonlinear regression model to compare the differences as the explanatory variables including the time variable, MSCI Morgan Taiwan index, and Taiwan stock price index (TAIEX). We find that the three kinds of return rates do not satisfy the Normal distribution assumption of regression analysis. Then we follow Wang and Lee (2019) to run nonlinear regression to find the optimal nonlinear estimators. There are three results. One is that the time variable can grab the oscillations of TF’s return rates before COVID-19 and the whole observation period. During COVID-19, the time variable did not display the oscillation effect of TF’s return rates. In contrast, MSCI Morgan Taiwan index’s return rates appeared the oscillation effect. Second, the return rates of TAIEX have invariable function form and coefficient sign. This implies that COVID-19 did not affect the positive...

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Mei-Yu LEE

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models

This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0), whatever the variance-covariance matrix of the errors...

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