scholarly journals ARCH GARCH METHOD OF FORECASTING CONSUMER PRICE INDEX (CPI) IN SEMARANG

2020 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Sri Kustiara ◽  
Indah Manfaati Nur ◽  
Tiani Wahyu Utami
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Indeks Harga Konsumen (IHK) merupakan salah satu indikator ekonomi penting yang dapat memberikan informasi mengenai perkembangan harga barang/jasa yang dibayar oleh konsumen di suatu wilayah. Penghitungan IHK ditujukan untuk mengetahui perubahan harga dari sekelompok tetap barang atau jasa yang umumnya dikonsumsi oleh masyarakat setempat. Dalam metode yang digunakan dalam pemodelan data runtun waktu memiliki syarat khusus yaitu yang  teridentifikasi efek heteroskedastisitas. Tujuan dari penelitian ini adalah untuk mengetahui model terbaik peramalan periode berikutnya serta hasil prediksi periode mendatang. Variabel yang digunakan adalah data Indeks Harga Konsumen dalam bulan. Sehingga untuk mengatasi permasalahan pada data penelitian ini digunakan metode Autoregressive Conditional Heteroscedasticity Generalized Autoregressive Conditional Heteroscedasticity (ARCH GARCH). Hasil dari penelitian ini didapatkan metode ARCH GARCH model terbaik yang digunakan adalah ARIMA (1,1,1)~GARCH (1,0). Dengan prediksi dari volatilitas dengan nilai standar deviasi 0.98283514 diperoleh prediksi volatilitas terendah sebesar 0.9632546 dan prediksi volatilitas tertinggi sebesar 0.9980155.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

scholarly journals Month of the Year Effect pada Pasar Obligasi di Indonesia

2017 ◽  
Vol 20 (2) ◽  
pp. 291
Author(s):  
Robiyanto Robiyanto
Keyword(s):  
Random Walk ◽  
Price Index ◽  
Weak Form ◽  
Bond Market ◽  
Conditional Heteroscedasticity ◽  
Year Effect ◽  
Analysis Technique ◽  
Bond Returns ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Positive Effect

<p><em>This study examines the month-of-the-year effect on the bond returns in Indonesia. I use the monthly closing price index (Indonesia Bond Indexes / INDOBeX) data for the periods of July 2003-July 2017 from Bloomberg. I then run the Generalize Autoregressive Conditional Heteroscedasticity (GARCH) analysis technique to analyze the data because the residuals exhibit a significant pattern of Autoregressive Conditional Heteroscedasticity (ARCH). The results show that only the month of July has a significantly positive effect on the bond returns; indicating that there is the month-of-the-year effect in the Indonesian bond market. Further, these also imply that the Indonesian bond market does not exhibit a random walk pattern and consequently they are inefficient in the weak form.</em></p><p><em><br /></em>Abstrak</p><p>Penelitian ini menguji pengaruh bulan-bulan perdagangan (month of the year) terhadap return obligasi di Indonesia. Data yang dipergunakan dalam penelitian ini adalah data indeks harga obligasi (Indonesia Bond Indexes / INDOBeX) penutupan bulanan selama periode Juli 2003 hingga Juli 2017 yang diperoleh dari Bloomberg. Analisis data dilakukan dengan menggunakan teknik analisis Generalize Autoregressive Conditional Heteroscedasticity (<em>GARCH</em>) karena pola residual yang dihasilkan menunjukkan adanya pola Autoregressive Conditional Heteroscedasticity (<em>ARCH</em>) yang signifikan. Hasil penelitian ini menunjukan bahwa bulan Juli memiliki pengaruh positif yang signifikan terhadap return obligasi di Indonesia. Sementara bulan-bulan lainnya tidak memiliki pengaruh terhadap return obligasi di Indonesia. Hasil ini menunjukkan bahwa terjadi month of the year effect di pasar obligasi di Indonesia. Temuan ini memiliki implikasi bahwa pasar obligasi di Indonesia tidak berjalan acak (random walk) sehingga tidak efisien dalam bentuk lemah.</p>

scholarly journals The Relationship between Stock Returns and Trading Hours: Evidence from Amman Stock Exchange

2018 ◽  
Vol 7 (3.21) ◽  
pp. 89
Author(s):  
Buthiena Kharabsheh ◽  
Mahera Hani Megdadi ◽  
Waheeb Abu-ulbeh
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Amman Stock Exchange ◽  
The Relationship ◽  
Autoregressive Conditional Heteroscedasticity

This study investigates the relationship between stock returns and trading hours for 22 shares listed on Amman Stock Exchange (ASE). We analyze the hourly trading data for the period Dec.2005 to Dec.2006. The two trading hours in ASE were split into four periods; first half of the first hour (10:00-10:30), second half of the first hour (10:30-11:00), first half of the second hour (11:00-11:30), and second half of the second hour (11:30-12:00). Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, our results reveal that the hourly trading time significantly affects stock returns.  

Peramalan Indeks Harga Saham dengan Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH)

2021 ◽  
Vol 1 (1) ◽  
pp. 7-12
Author(s):  
Nur Najmi Layla ◽  
Eti Kurniati ◽  
Didi Suhaedi
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Data Partitioning ◽  
Forecasting Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Eligibility Requirements ◽  
The Garch Model ◽  
Autoregressive Conditional Heteroscedasticity

Abstract. The stock price index is the information the public needs to know the development of stock price movements. Stock price forecasting will provide a better basis for planning and decision making. The forecasting model that is often used to model financial and economic data is the Autoregressive Moving Average (ARMA). However, this model can only be used for data with the assumption of stationarity to variance (homoscedasticity), therefore an additional model is needed that can model data with heteroscedasticity conditions, namely the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This study uses data partitioning in pre-pandemic conditions and during the pandemic, Insample data with pre-pandemic conditions and insample data during pandemic conditions. Based on the research results, the GARCH model (1,1) was obtained with the conditions before the pandemic and GARCH (1,2) during the pandemic condition. The forecasting model obtained has met the eligibility requirements of the GARCH model. If the forecasting model fulfills the eligibility requirements, then MAPE calculations are performed to see the accuracy of the forecasting model. And obtained MAPE in the conditions before the pandemic and during the pandemic in the very good category. Abstrak. Indeks harga saham merupakan informasi yang diperlukan masyarakat untuk mengetahui perkembangan pergerakan harga saham. Peramalan harga saham akan memberikan dasar yang lebih baik bagi perencanaan dan pengambilan keputusan. Model peramalan yang sering digunakan untuk memodelkan data keuangan dan ekonomi adalah Autoregrresive Moving Average (ARMA). Namun model tersebut hanya dapat digunakan untuk data dengan asumsi stasioneritas terhadap varian (homoskedastisitas), oleh karena itu diperlukan suatu model tambahan yang bisa memodelkan data dengan kondisi heteroskedastisitas, yaitu model Generalized Autoregressive Conditional Heteroscedastisity (GARCH). Penelitian ini menggunakan partisi data pada kondisi sebelum pandemi dan saat pandemi berlangsung data Insample dengan kondisi sebelum pandemi dan insample pada kondisi pandemi. Berdasarkan hasil penelitian, maka didapat model GARCH (1,1) dengan kondisi sebelum pandemi dan GARCH (1,2) saat kondisi pandemi. Model peramalan yang didapat sudah memenuhi syarat kelayakan model GARCH. Apabila model peramalan terpenuhi syarat kelayakannya maka dilakukan perhitungan MAPE untuk melihat keakuratan model peramalannya. Dan diperoleh MAPE pada kondisi sebelum pandemi dan saat pandemi dengan kategori sangat baik. 

scholarly journals A Robust Algorithm for Classification and Diagnosis of Brain Disease Using Local Linear Approximation and Generalized Autoregressive Conditional Heteroscedasticity Model

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1268 ◽  
Author(s):  
Ali Hamzenejad ◽  
Saeid Jafarzadeh Ghoushchi ◽  
Vahid Baradaran ◽  
Abbas Mardani
Keyword(s):  
Linear Approximation ◽  
Garch Model ◽  
Brain Disease ◽  
High Rate ◽  
Discrete Wavelet ◽  
Conditional Heteroscedasticity ◽  
Robust Algorithm ◽  
Local Linear ◽  
The Garch Model ◽  
Autoregressive Conditional Heteroscedasticity

Regions detection has an influence on the better treatment of brain tumors. Existing algorithms in the early detection of tumors are difficult to diagnose reliably. In this paper, we introduced a new robust algorithm using three methods for the classification of brain disease. The first method is Wavelet-Generalized Autoregressive Conditional Heteroscedasticity-K-Nearest Neighbor (W-GARCH-KNN). The Two-Dimensional Discrete Wavelet (2D-DWT) is utilized as the input images. The sub-banded wavelet coefficients are modeled using the GARCH model. The features of the GARCH model are considered as the main property vector. The second method is the Developed Wavelet-GARCH-KNN (D-WGK), which solves the incompatibility of the WGK method for the use of a low pass sub-band. The third method is the Wavelet Local Linear Approximation (LLA)-KNN, which we used for modeling the wavelet sub-bands. The extracted features were applied separately to determine the normal image or brain tumor based on classification methods. The classification was performed for the diagnosis of tumor types. The empirical results showed that the proposed algorithm obtained a high rate of classification and better practices than recently introduced algorithms while requiring a smaller number of classification features. According to the results, the Low-Low sub-bands are not adopted with the GARCH model; therefore, with the use of homomorphic filtering, this limitation is overcome. The results showed that the presented Local Linear (LL) method was better than the GARCH model for modeling wavelet sub-bands.

Pengaruh Tingkat Inflasi, Tingkat Suku Bunga Bi, Dan Nilai Tukar Usd-Idr Terhadap Perubahan Harga Saham Sektor Perusahaan Manufaktur Di Indonesia

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Adi Cahya Stefanus ◽  
Robiyanto Robiyanto
Keyword(s):  
Exchange Rate ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Manufacturing Sector ◽  
Analysis Method ◽  
Conditional Heteroscedasticity ◽  
Macroeconomic Factors ◽  
Stock Price Index ◽  
Autoregressive Conditional Heteroscedasticity

The objective of this study is to find out how macroeconomic factors such as exchange rate, BI rate and inflation rate can affect the manufacturing  sector stock price index in IDX from 2011 until 2018. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) is used as the analysis method in this research to find the fittest model. The result, only exchange rate that no significant effect to manufacturing sector stock, price index, Inflation and BI rate have significant effect to manufacturing sector stock price index.

scholarly journals MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY IN MEAN UNTUK MERAMALKAN VOLATILITAS RETURN SAHAM

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Syarifah Zela Hafizah, Dadan Kusnandar, Shantika Martha
Keyword(s):  
Time Series ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Volatility Clustering ◽  
Model Time Series ◽  
Autoregressive Conditional Heteroscedasticity

Volatilitas menunjukkan fluktuasi pergerakan harga saham. Semakin tinggi volatilitas maka semakin tinggi pula kemungkinan mengalami keuntungan dan kerugian. Data time series yang sering memiliki volatilitas yang tinggi adalah data keuangan. Data time series di bidang keuangan sering memiliki sifat volatility clustering atau sering disebut sebagai kasus heteroskedastisitas. Pada umumnya, pemodelan data time series harus memenuhi asumsi varian konstan (homoskedastisitas). Untuk mengatasi masalah heteroskedastisitas, model time series yang dapat digunakan adalah ARCH/GARCH. Model GARCH merupakan pengembangan dari model ARCH yang dapat digunakan untuk menggambarkan sifat dinamik volatilitas dari data. Salah satu bentuk pengembangan dari model GARCH adalah Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M). Tujuan dari penelitian ini adalah untuk mengimplementasikan model GARCH-M pada peramalan volatilitas return saham. Data yang digunakan dalam penelitian ini adalah return penutupan harga saham mingguan S&P 500 dari September 2013 sampai Juni 2019. Model terbaik yang digunakan untuk peramalan volatilitas pada return harga saham S&P 500 adalah MA (1) GARCH (1,1)-M.Kata Kunci: saham, volatilitas, GARCH-M

scholarly journals Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models

2016 ◽  
Vol 13 (4) ◽  
pp. 203-211 ◽  
Author(s):  
Adebayo Augustine Kutu ◽  
Harold Ngalawa
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Jel Classification ◽  
Conditional Heteroscedasticity ◽  
United States Dollar ◽  
Global Shocks ◽  
Student’S T ◽  
T Distribution ◽  
Autoregressive Conditional Heteroscedasticity

This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering January 1994 – December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia, compared to the APARCH model. Keywords: exchange rate volatility, global Shocks, GARCH and APARCH models. JEL Classification: F30, F31, P33

scholarly journals PENERAPAN MODEL GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) DALAM MENGHITUNG NILAI BETA SAHAM INDEKS PEFINDO25

2018 ◽  
Vol 18 (2) ◽  
pp. 67
Author(s):  
Febrifke Adria Kanal ◽  
Tohap Manurung ◽  
Jantje D Prang
Keyword(s):  
Positive Result ◽  
Systematic Risk ◽  
Garch Model ◽  
Stock Index ◽  
Conditional Heteroscedasticity ◽  
Closing Price ◽  
Autoregressive Conditional Heteroscedasticity

PENERAPAN MODEL GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) DALAM MENGHITUNG NILAI BETA SAHAM INDEKS PEFINDO25ABSTRAKSaat ini banyak orang yang berfikir untuk berinvestasi. Investasi adalah kegiatan yang mempunyai dua hasil, yaitu positif (return) dan negatif (risiko). Risiko saham khususnya risiko sistematik merupakan salah satu informasi penting yang dibutuhkan investor dalam melakukan investasi. Risiko sistematik dapat diukur dengan beta. Penelitian ini bertujuan untuk mengestimasi nilai beta saham dengan menggunakan model GARCH. Penelitian dilakukan di Universitas Sam Ratulangi dan berlangsung selama 4 bulan sejak November 2017 sampai maret 2018. Data yang digunakan adalah data closing price  harian saham periode 1 Agustus 2016 – 31 Juli 2017. Model GARCH yang dipakai adalah GARCH(1,1) untuk ARNA, GARCH(1, 1) untuk SMSM, dan GARCH(1,4) untuk TOTL. Nilai beta yang diperoleh yaitu untuk ARNA,  untuk SMSM dan  untuk TOTL.Kata Kunci :  Saham, GARCH, Beta. APPLICATION OF GARCH ( GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY ) MODEL  IN CALCULATING BETA VALUE OF STOCK INDEX PEFINDO25ABSTRACTIn this time many peoples are thinking to make an investation. Investment is an activity that has two results, a positive result (return) and a negative result (risk). Stock risk, especially systematic risk is one of the important information that investors need to know in investing. Systematic risk can be measured with beta. This study aims to estimate the value of beta stock by using GARCH model. This study was conducted at Sam Ratulangi University and lasted for 4 months since November 2017 until March 2018. The data used is the daily closing price on period 1 August 2016 – 31 July 2017. GARCH model used is GARCH (1, 1) for ARNA, GARCH (1, 1) for SMSM, GARCH (1,4) for TOTL. Beta values obtained are ARNA,     for SMSM dan  for TOTL.Keywords  :  Stock, GARCH, Beta.

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