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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) International Journal of Computing Science and Applied Mathematics-IJCSAM
Hanna Arini Parhusip
Master Program in Data Science, Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Mathematics Physics

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Modeling and Estimating GARCH-X and Realized GARCH Using ARWM and GRG Methods Didit Budi Nugroho; Melina Tito Wijaya; Hanna Arini Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 1 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i1.4309

Abstract

This study evaluates the fitting performance of GARCH-X(1,1) and RealGARCH(1,1) models, which are extensions of GARCH(1,1) model by adding the Realized Kernel measure as an exogenous component, on real data, namely the Financial Times Stock Exchange 100 and Hang Seng stock indices over the period from January 2000 to December 2017. The models assume that the return error follows Normal and Student- t distributions. The parameters of models are estimated by using the Adaptive Random Walk Metropolis (ARWM) method implemented in Matlab and the Generalized Reduced Gradient (GRG) method. The comparison of estimation results shows that the GRG method has a good ability to estimate the models because it provides the estimation results that are close to the results of the ARWM method in terms of relative error. On the basis of Akaike Information Criterion, the RealGARCH models perform better than the GARCH-X models, where the RealGARCH model with Student- t distribution provides the best fit.
A Hierarchical Bayesian Model of Multi-Hazard Impacts on Property Prices in the Jakarta Metropolitan Area Fachrurrozi; Jordi Enal Ambat; Hanna Arini Parhusip; Suryasatriya Trihandaru
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12717

Abstract

This study examines the complex relationship between multi-hazard disaster risks and property prices in the Jakarta Metropolitan Area, one of the world's most disaster-prone urban regions. The research investigates how various natural hazards, including floods, earthquakes, and other environmental risks, influence real estate values across 138 districts encompassing 15,758 property data. This study pioneers the integration of hierarchical Bayesian modeling with causal machine learning techniques to quantify multi-hazard impacts on property prices, providing the first comprehensive analysis of disaster risk interactions in Indonesian real estate markets. We employ methodological triangulation across Bayesian inference, causal forests, and spatial econometrics to ensure robust causal identification. We employ a multi-methodological approach combining spatial analysis, hierarchical Bayesian modeling, and causal forest algorithms on a dataset of 15,758 properties. The analysis includes Moran's I for spatial autocorrelation (0.73 for risks, 0.65 for prices), PyMC for Bayesian inference with 12,000 MCMC samples, and EconML for causal effect estimation with heterogeneous treatment effects. Properties with high disaster risk experience an 12.2% price discount (95% CI: -20.5%, -3.7%), with each unit increase in average risk score reducing prices by 4.3% (95% CI: -7.9%, -0.4%). Spatial clustering is highly significant (Moran's I = 0.73, p < 0.001). Heterogeneous effects reveal progressive impacts from 3.2% in bottom quintile to 9.4% in top quintile. Policy simulation demonstrates that comprehensive flood mitigation could increase total property values by 840.6 billion IDR, generating an average price increase of 14.8% with benefit-cost ratio exceeding 3:1.
Co-Authors Didit Budi Nugroho Fachrurrozi Jordi Enal Ambat Melina Tito Wijaya Suryasatriya Trihandaru