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GRG Non-Linear and ARWM Methods for Estimating the GARCH-M, GJR, and log-GARCH Models Nugroho, Didit Budi; Panjaitan, Lam Peter; Kurniawati, Dini; Kholil, Zaini; Susanto, Bambang; Sasongko, Leopoldus Ricky
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 2 (2022): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i2.7694

Abstract

Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models. The first purpose of this study is to provide the capability analyze of the GRG non-linear method built in Excel’s Solver to estimate the GARCH models in comparison to the adaptive random walk Metropolis method in Matlab by own codes. The second contribution of this study is to evaluate some characteristics and performance of the GARCH-M(1,1), GJR(1,1), and log-GARCH(1,1) models with Normal and Student-t error distributions that fitted to financial data. Empirical analyze is based on the application of models and methods to the DJIA, S&P500, and S&P CNX Nifty stock indices. The first empirical result showed that Excel’s Solver’s Generalized Reduced Gradient (GRG) non-linear method has capability to estimate the econometric models. Second, the GJR(1,1) models provide the best fitting, followed by the GARCH-M(1,1), GARCH(1,1), and log-GARCH(1,1) models. This study concludes that Excel’s Solver’s GRG non-linear can be recommended to the practitioners that do not have enough knowledge in the programming language in order to estimate the econometrics models. It also suggests to incorporate a risk premium in the return equation and an asymmetric effect in the variance equation. 
Understanding Nurses’ Caring Behavior: The Impact of Work Environment and Individual Factors Afandi, Alfid Tri; Ardiana, Anisah; Muhammad Nur, Kholid Rosyidi; Sutawardana, Jon Hafan; Rasni, Hanny; Sulistyorini, Lantin; Kurniawati, Dini
Jurnal Keperawatan Soedirman Vol 20 No 3 (2025): Jurnal Keperawatan Soedirman (JKS)
Publisher : Fakultas Ilmu-ilmu Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jks.2025.20.3.13689

Abstract

Nurse caring behavior is essential for the quality of healthcare services and patient well-being, yet several internal and external factors influence this behavior. This study analyzes the effects of work rewards, workload, motivation, and personality on nurses’ caring behavior. This cross-sectional study used a quantitative approach. Data were collected from 217 nurses in hospitals using structured questionnaires measuring variables of work rewards, workload, motivation, personality, and caring behavior. Data analysis was performed using descriptive statistics and multiple regression analysis. The findings show that motivation has the most significant influence on caring behavior (β = 0.45, p < 0.01), followed by work rewards (β = 0.30, p < 0.01) and personality (β = 0.20, p < 0.01). Workload has a significant negative effect (β = -0.28, p < 0.01). An R-squared value of 0.65 indicates that these four variables can explain 65% of the variability in caring behavior. These findings support Gibson’s theory that environmental factors (work rewards, workload) and individual factors (motivation, personality) affect caring behavior. Motivation and work rewards drive caring behavior, while a high workload hinders it. Policies that enhance motivation and work rewards, along with balanced workload management, are necessary to support caring behavior in nurses Keywords: Caring behavior, Work rewards, Workload, Motivation, Personality.