Metode Peramalan Deret Waktu Menggunakan Model Asymmetric Power Autoregressive Conditional Heteroskedastic (APARCH)

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Dian Kurniasari ◽  
Hana Ayu Masha ◽  
Mustofa Usman
Keyword(s):  
Confidence Interval ◽  
Data Model ◽  
Stock Price ◽  
Economic Systems ◽  
Average Model ◽  
Conditional Heteroscedasticity ◽  
Conditional Average ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Era globalisasi menyebabkan banyak perubahan dalam pengembangan sistem ekonomi, salah satunya adalah data keuangan. Tujuan dari penelitian ini ialah untuk mendapatkan model terbaik dalam menganalisis dan memprediksi data penutupan harga saham mingguan untuk PT Adhi Karya (Persero) Tbk dari September 1990 hingga Januari 2016 yang berjumlah 1314 data. Model yang digunakan adalah model Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH). Model terbaik dipilih berdasarkan Akaike Info Criterion (AIC) dan Schwarz Criterion (SC). Dari hasil analisis diperoleh model terbaik yaitu APARCH (1,1) dengan ARIMA (1,1,1) sebagai model rerata bersyarat. Hasil peramalan untuk 7 periode berikutnya menunjukkan bahwa perkiraan tersebut dalam interval kepercayaan 95% yang berarti bahwa hasil peramalan menggunakan model ini dapat dipercaya dalam kisaran 95%.   The era of globalization led to many changes in the development of economic systems, one of which is the data that is financially. The purpose of this study is to get the best model in analyzing and predicting weekly stock price closing data for PT Adhi Karya (Persero) Tbk from September 1990 to January 2016 which amounted to 1314 data. The model used is the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. The best models are selected based on Akaike Info Criterion (AIC) and Schwarz Criterion (SC). From the analysis result obtained the best model that is APARCH (1,1) with ARIMA (1,1,1) as conditional average model. The result of forecasting for the next 7 periods shows that the forecast is within a 95% confidence interval which means that the forecasting result using this model can be trusted in the 95% range.    

scholarly journals Analisis Risiko Investasi Saham Syariah Dengan Model Value AT Risk-Asymmetric Power Autoregressive Conditional Heterocedasticity (VaR-APARCH)

2017 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Syarif Hidayatullah ◽  
Mohammad Farhan Qudratullah
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Heteroscedasticity ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Penelitian ini membahas analisis risiko data runtun waktu dengan model Value at Risk- Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH)dalam pasar modal syariah. Metode yang digunakan dalam penelitian ini adalah penerapan kasus.Data yang digunakan adalah harga penutupan harian saham dalam Jakarta Islamic Index (JII)periode 4 Maret 2013 sampai 8 April 2015.Model APARCH yang dipilih berdasarkan nilai Schwarz Criterion (SC).Langkah-langkah dalam penelitian ini adalah menguji kestasioneran data, mengidentifikasi model ARIMA,mengestimasi parameter model ARIMA, menguji diagnostik model ARIMA, mendeteksi ada tidaknya unsur ARCH atau unsur heteroskedastisitas, uji asimetris data saham, mengestimasi model APARCH, menguji diagnostik model APARCH, dan menghitung risiko dengan VaR-APARCH.Model terbaik yang dipilih adalah ARIMA ((3),0,0) dan APARCH (1,1). Model ini valid untuk menganalisis besar risiko investasi dalam jangka waktu 10 hari ke depan.

scholarly journals PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL THRESHOLD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ervina, Dadan Kusnandar, Nurfitri Imro’ah
Keyword(s):  
Akaike Information Criterion ◽  
Information Criterion ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Model Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) merupakan model yang digunakan untuk memodelkan volatilitas yang memiliki efek asimetris. Tujuan penelitian ini adalah memodelkan dan meramalkan volatilitas IHSG menggunakan model TGARCH untuk sepuluh periode ke depan. Data yang digunakan adalah data return IHSG penutupan mingguan dari tanggal 8 Februari 2009 sampai dengan 10 Februari 2019. Penelitian ini diawali dengan pembentukan model Box Jenkins. Residual model Box Jenkins terbaik digunakan untuk mendeteksi heteroskedastisitas menggunakan uji ARCH-LM. Data residual yang memiliki heteroskedastisitas dimodelkan ke dalam model GARCH. Residual model GARCH dan residual model Box Jenkins digunakan untuk memeriksa pengaruh asimetris, yaitu dengan melakukan korelasi silang pada kedua residual model tersebut. Berdasarkan hasil korelasi silang yang dilakukan didapatkan adanya pengaruh asimetris terhadap volatilitas, sehingga digunakan model TGARCH untuk mengatasinya. Model TGARCH terbaik dalam penelitian ini adalah TGARCH(1,1) berdasarkan nilai Akaike Information Criterion (AIC) dan Schwarz Criterion (SC) terkecil. Model TGARCH(1,1) digunakan untuk meramalkan volatilitas IHSG. Hasil peramalan volatilitas yang diperoleh untuk sepuluh periode ke depan mengalami peningkatan sebesar 0,000015 sampai dengan 0,000029.Kata Kunci: Asimetris, GARCH, TGARCH

scholarly journals PEMODELAN VOLATILITAS SAHAM MENGGUNAKAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Merista Dominika Br Pandia, Naomi Nessyana Debataraja, Shantika Martha
Keyword(s):  
Central Asia ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH) merupakan generalisasi dari model Autoregressive Conditional Heteroscedasticity (ARCH). Model GARCH digunakan untuk memodelkan volatilitas pada return saham yang memiliki heteroskedastisitas. Namun model GARCH mengabaikan efek asimetris pada volatilitas sehingga ditemukan model Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH). Model APARCH digunakan untuk memodelkan volatilitas yang memiliki efek asimetris. Efek asimetris dapat dilihat dari cross correlogram dengan melakukan korelasi silang residual kuadrat model Box-Jenkins dan residual model GARCH. Tujuan penelitian ini adalah untuk menentukan model APARCH return saham Bank Central Asia (BCA) pada 4 Juni 2015 sampai dengan 28 Maret 2018. Hasil penelitian menunjukkan model terbaik Box-Jenkins adalah model AR(3). Residual kuadrat model AR(3) digunakan untuk melakukan uji heteroskedastisitas sedangkan residual model GARCH(1,1) digunakan untuk uji efek asimetris. Model APARCH terbaik yang diperoleh adalah APARCH (1,1). Kata Kunci: Asimetris, GARCH, APARCH

scholarly journals Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Irwan Kasse ◽  
Andi Mariani ◽  
Serly Utari ◽  
Didiharyono D.
Keyword(s):  
Risk Analysis ◽  
Value At Risk ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Investment Risk ◽  
Conditional Heteroscedasticity ◽  
Additional Information ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

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 Penerapan Model ARIMA-ARCH untuk Meramalkan Harga Saham PT. Indofood Sukses Makmur Tbk

2021 ◽  
Vol 3 (3) ◽  
pp. 171-177
Author(s):  
Yulvia Fitri Rahmawati ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Abstract– The stock price is the value of the stock in the market that fluctuates from time to time. Time series data in the financial sector generally have quite high volatility which can cause heteroscedasticity problems. This study aims to model and to predict the stock price of PT Indofood Sukses Makmur Tbk using the ARIMA-ARCH model. The data used is daily stock prices from 2nd June 2020 to 15th February 2021 as training data, while from 16th February 2021 to 1st March 2021 as testing data. ARIMA-ARCH model is a model that combines Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH), which can be used to overcome the residues of the ARIMA model which are indicated to have heteroscedasticity problems. The result showed that the model that could be used was ARIMA(1,1,2)-ARCH(1). This model can provide good forecasting result with a relatively small MAPE value of 0.515785%. Abstrak– Harga saham adalah nilai saham di pasar yang berfluktuasi dari waktu ke waktu. Data runtun waktu di sektor keuangan umumnya memiliki volatilitas cukup tinggi yang dapat menyebabkan masalah heteroskedastisitas. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga saham PT Indofood Sukses Makmur Tbk menggunakan model ARIMA-ARCH. Data yang digunakan adalah harga saham harian dari 2 Juni 2020 hingga 15 Februari 2021 sebagai data training, sedangkan dari 16 Februari 2021 hingga 1 Maret 2021 sebagai data testing. Model ARIMA-ARCH merupakan suatu model yang menggabungkan Autoregressive Integrated Moving Average (ARIMA) dan Autoregressive Conditional Heteroscedasticity (ARCH), yang dapat digunakan untuk mengatasi residu dari model ARIMA yang terindikasi memiliki masalah heteroskedastisitas. Hasil penelitian menunjukkan bahwa model yang dapat digunakan adalah ARIMA(1,1,2)-ARCH(1). Model tersebut mampu memberikan hasil peramalan yang baik dengan perolehan nilai MAPE yang relatif kecil yaitu 0,515785%.

scholarly journals PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk)

2018 ◽  
Vol 7 (2) ◽  
pp. 110-118
Author(s):  
Dea Manuella Widodo ◽  
Sudarno Sudarno ◽  
Abdul Hoyyi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Step Function ◽  
Series Data ◽  
Model Data ◽  
Conditional Heteroscedasticity ◽  
Constant Variance ◽  
High Volatility ◽  
Autoregressive Conditional Heteroscedasticity

The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous).  In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH 

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 Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion

INSIST ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Eri Setiawan ◽  
Netti Herawati ◽  
Khoirin Nisa
Keyword(s):  
Stock Return ◽  
Information Criterion ◽  
Performance Comparison ◽  
Conditional Heteroscedasticity ◽  
Stock Return Volatility ◽  
Asymmetric Volatility ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Data

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.

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