Comparison of Kernel regression model with a polynomial regression model on financial data

Abstract

Regression analysis is constructed for determining the influence of independent variables on the dependent variable. It can be done by looking at the relationship between those variables. This task of approximating the mean function can be done essentially in two approaches, parametric and nonparametric approach. Kernel regression is one of the models with a nonparametric approach, and polynomial quadratic regression is one of the models with the parametric approach. This research aims to find the best model regression with compare to the model of kernel regression and model of polynomial quadratic regression in financial data using RMSE criterion. Share data that be used is Mastercard Incorporated (MA) with data periods 02 Januari 2019 until 31st December 2019. Research's result indicated that for MA data, best model regression is kernel regression with RMSE value = 16,00147 and Bandwidth (h) = 25,64.