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Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion Setiawan, Eri; Herawati, Netti; Nisa, Khoirin
INSIST Vol 3, No 2 (2018)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/ins.v3i2.160

Abstract

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.
Testing Normality and Bandwith Estimation Using Kernel Method For Small Sample Size Netti Herawati; Khoirin Nisa
Jurnal ILMU DASAR Vol 10 No 1 (2009)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.124 KB)

Abstract

This article aimed to study kernel method for testing normality and to determine the density function based on curve fitting technique (density plot) for small sample sizes. To obtain optimal bandwith we used Kullback-Leibler cross validation method. We compared the result using goodness of fit test by Kolmogorof Smirnov test statistics. The result showed that kernel method gave the same performance as Kolmogorof Smirnov for testing normality but easier and more convinient than Kolmogorof Smirnov does.
Robust Biplot Analysis of Natural Disasters in Indonesia from 2019 To 2021 Hilda Venelia; Khoirin Nisa; Rizki Agung Wibowo; Mona Arif Muda
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 13 No 2 (2021): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v13i2.349

Abstract

Indonesia is one of the most natural disaster-prone countries in the world, frequently exposed to a range of hazards. Currently, Indonesia has 34 provinces and natural disasters that occur in each province are different, therefore it is necessary to analyze the mapping of natural disasters that often occur in each province to provide scientific analysis for risk management of the natural disasters. One of the quick steps in describing data that can be used is biplot analysis, as biplot analysis can describe a lot of data then summarized it into the form of a two-dimensional graph. The aim of this research is to map 34 provinces in Indonesia based on the incidence of natural disasters from 2019 to 2021 using robust biplot analysis. Based on the result, robust biplot analysis can explain 87,9% of the information on natural disasters in every province in Indonesia. Lampung, Bengkulu, Bangka Belitung, Special Region of Yogyakarta, North Sulawesi, West Sulawesi, Southeast Sulawesi, Gorontalo, East Nusa Tenggara, Bali, Maluku, West Maluku, Papua, and West Papua are provinces that have similar natural disaster characteristics. Flood, tornado and forest and land fires are natural disasters that often occur in Indonesia. The provinces that have the highest risk of flood, landslide, and tornado were West Java, Central Java, and East Java. Then, the provinces with the highest risk of forest and land fires were Aceh and South Kalimantan.
Modeling with generalized linear model on covid-19: Cases in Indonesia Saidi, Subian; Herawati, Netti; Nisa, Khoirin
International Journal of Electronics and Communications Systems Vol 1, No 1 (2021): International Journal of Electronics and Communications System
Publisher : Raden Intan State Islamic University of Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.202 KB)

Abstract

The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distribution. Where the three distributions will be compared to get the best model. The variables used in this study were the number of confirmed Covid-19 cases per day and the number of deaths due to Covid-19 per day. This study also aims to see how much influence the confirmation of Covid-19 has on the number of deaths due to Covid-19 per day. By using 3 types of exponential family distribution, the best result is the Gaussian distribution GLM. Selection of the best model using Akaike Information Criterion (AIC).