COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)

  • Yuniar Farida Department of Mathematics, Science and Technology Faculty, UIN Sunan Ampel Surabaya
  • Ida Purwanti Department of Mathematics, Science and Technology Faculty, UIN Sunan Ampel Surabaya
  • Nurissaidah Ulinnuha Department of Mathematics, Science and Technology Faculty, UIN Sunan Ampel Surabaya
DOI: https://doi.org/10.30598/barekengvol16iss1pp321-330
Keywords: Epanechnikov kernel, Gaussian kernel, Nadaraya-Watson estimator, Nonparametric regression, ISSI

Abstract

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.

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Published
2022-03-21
How to Cite
[1]
Y. Farida, I. Purwanti, and N. Ulinnuha, “COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)”, BAREKENG: J. Math. & App., vol. 16, no. 1, pp. 323-332, Mar. 2022.
Issue
Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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