Abstract
During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.
Highlights
Selama pandemi Covid-19, pasar saham Indonesia mengalami berbagai tekanan besar yang menyebabkan nilai Indeks Harga Saham Gabungan (IHSG) sangat berfluktuasi
This study aims to design the best model in describing the relationship between Jakarta Composite Index (JCI) value and BI 7-Days Repo Rate
The results showed that the parametric regression models failed to meet the classical assumptions
Summary
Pandemi Covid-19 menciptakan perubahan dalam tatanan kehidupan masyarakat di berbagai negara, termasuk di Indonesia. Hal tersebut sangat berguna agar pemerintah dapat menetapkan kebijakan yang tepat dalam mengendalikan nilai IHSG, khususnya dengan mengatur BI 7-Days Repo Rate. Digunakan sebuah pendekatan baru dalam melihat hubungan antara BI 7-Days Repo Rate dengan IHSG yaitu melalui pendekatan regresi nonparametrik. Perbandingan terhadap kedua pendekatan tersebut sangat penting untuk dilakukan guna mendapatkan pendekatan yang lebih baik dalam pemodelan BI 7-Days Repo Rate terhadap IHSG sehingga interpretasi yang diperoleh lebih akurat. Dengan membandingkan model regresi yang diperoleh dari dua pendekatan tersebut, maka model terbaik dengan tingkat akurasi yang tinggi dapat diperoleh sehingga interpretasi terhadap pengaruhnya dapat dianalisis dengan baik. Hasil penelitian ini sangat bermanfaat bagi pengembangan model statistika yang optimal sehingga dapat dijadikan acuan dalam menetapkan kebijakan terkait BI 7-Days Repo Rate dalam pengaruhnya terhadap IHSG
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