Dede Zumrohtuliyosi
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PEMODELAN INFLASI BERDASARKAN HARGA-HARGA PANGAN MENGGUNAKAN SPLINE MULTIVARIABEL Prahutama, Alan; Utama, Tiani Wahyu; Caraka, Rezzy Eko; Zumrohtuliyosi, Dede
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.36 KB) | DOI: 10.14710/medstat.7.2.89-94

Abstract

Inflation is defined as a sustained increase in the general level of price for goods and services. Some of the events that led to inflation in Indonesia is rising fuel prices, rising prices of meat and chili. Inflation has negative impact, because decreased purchasing power.  So that the inflation model is needed. Modeling inflation can be use regression models. The approach can be performed with nonparametric regression, one of method of nonparametric regression is spline method. In this case, use three predictors to modeling inflation using spline multivariable. The predictors are price of rice, price of chicken, and price of chili. Obtained multivariable spline models with R-square of 93.94% with optimal m = 2 (quadratic) for 1 knots. Keywords: Spline Multivariable, GCV, Inflation
PENENTUAN VALUE AT RISK SAHAM KIMIA FARMA PUSAT MELALUI PENDEKATAN DISTRIBUSI PARETO TERAMPAT Zumrohtuliyosi, Dede; Hoyyi, Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Each investment object being traded in the stock market will get return that it has risk potential. Return and risk has mutual correlation that equilibrium. If the risk is high, then it obtains high return and vice versa. Risk management is the desain and implementation procedure for controlling risk. Value at Risk (VaR) is instrument to analyze risk management. Financial time series data for return data is assumed that it has heavy tail distribution and heteroscedasticity case (volatility clustering). Time series model that used to modelling this condition are Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregresive Conditional Heteroscedasticity (GARCH), while Value at Risk calculation is used Generalized Pareto Distribution (GPD) approach. This research uses return data from stock closing prices of Kimia Farma Pusat period October 2009 until September 2014. The best ARCH-GARCH model is ARIMA(0,1,1) GARCH(1,2) model because the parameters are significant and it has the smallest AIC value. Risk calculation that is gotten with GPD approach if invest in Kimia Farma Pusat with interval confidence 95% is 13.6928% rupiah from current asset.                  Keywords: Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Generalized Pareto Distribution (GPD), Value at Risk (VaR)