Perbandingan Analisis Regresi Linear dengan Analisis Regresi Copula pada Data Keuangan - Alfi Khairiati; Retno Budiarti; I Gusti Putu Purnaba (Department of Mathematics, Universitas Negeri Gorontalo, 2022)
Regression analysis is a statistical analysis to predict or explain the relationship between the response variable and one or more explanatory variables. The simplest and most commonly used regression analysis is linear regression. Copula regression is often used as an alternative method to overcome the problem of violating the assumptions of the linear regression model. The advantage of using copula regression is that the response variable does not have to follow a certain distribution and copula regression can explain nonlinear relationships. In this study, the copula used is the Gaussian copula and the student’s t copula. The main objective of this study is to compare the results of linear regression analysis with copula regression on financial data. In the linear regression method, the objective is to determine the estimated value of the response variable and to analyze the effect of macroeconomic factors on BMRI’s stock price. Meanwhile, copula regression was used to estimate the copula parameters using the Maximum Likelihood Estimation method and to determine the estimated value of the response variable using the Monte Carlo method. The measure of the accuracy of the model used is MAPE (Mean Absolute Percentage Error). This study uses financial data consisting of BMRI stock price data as response variables, as well as IHSG and the rupiah exchange rate as explanatory variables. The results showed that the MAPE values for linear regression and copula regression were small and not significantly different, meaning that both regressions were quite good in modeling financial data.