scholarly journals Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention

2020 ◽  
Vol 10 (05) ◽  
pp. 810-831
Author(s):  
Lawrence Dhliwayo ◽  
Florance Matarise ◽  
Charles Chimedza
Keyword(s):  
Moving Average ◽  
Level Shift ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

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. 

A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India

2019 ◽  
Vol 46 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Buvanesh Chandrasekaran ◽  
Rajesh H. Acharya
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Exchange Traded Funds ◽  
Volatility Spillover ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications

Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

scholarly journals PERBANDINGAN RESIKO INVESTASI BANK CENTRAL ASIA DAN BANK MANDIRI MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)

2016 ◽  
Vol 5 (4) ◽  
pp. 80
Author(s):  
Nurul Saadah ◽  
Maiyastri . ◽  
Hazmira Yozza
Keyword(s):  
At Risk ◽  
Central Asia ◽  
Value At Risk ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Abstrak. Data return saham adalah salah satu data deret waktu. Jika ingin melakukanpemodelan return, maka dapat dilakukan pemodelan deret waktu. Model rataan returnmenggunakan model Autoregressive Moving Average (ARMA). Sedangkan untuk memodelkanragam digunakan model Generalized Autoregressive Conditional Heteroscedasticity(GARCH). Setelah melakukan beberapa tahapan diperoleh model ARMA(1,0) danGARCH(1,1) sebagai model terbaik untuk data return saham Bank Central Asia. Sedangkanmodel terbaik untuk data return saham Bank Mandiri adalah model ARMA(0,1)dan GARCH(1,1). Model yang diperoleh digunakan untuk melakukan peramalan returndan volatilitas dalam pengukuran resiko. Salah satu alat ukur yang digunakan untukmengukur resiko adalah Value at Risk. Dari perhitungan resiko untuk kedua bank diperolehbahwa resiko maksimum Bank Mandiri lebih besar dari resiko maksimum BankCentral Asia.

scholarly journals STUDI PERBANDINGAN MODEL ARIMA DAN GARCH UNTUK MEMPREDIKSI HARGA SAHAM PADA PERUSAHAAN TAMBANG DI INDONESIA

2020 ◽  
Vol 10 (1) ◽  
pp. 83-98
Author(s):  
Muhammad Tharmizi Junaid ◽  
Ahmad Juliana ◽  
Hardianti Sabrina
Keyword(s):  
Moving Average ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Dalam berinvestasi para investor menggunakan alat statistik salah satunya adalah peramalan. Peramalan dilakukan oleh investor untuk memperlancar transaksi, meningkatkan keuntungan ataupun meminimalisir terjadinya kerugian. Dengan melakukan peramalan, investor diharapkan dapat membuat keputusan investasi yang tepat. Penelitian ini bertujuan untuk mengetahui model peramalan yang akurat untuk meramalkan harga saham PT. Adaro Energy (ADRO) dan saham PT. Bukit Asam  (PTBA) periode data selama 10 tahun sejak Oktober 2008 sampai dengan Oktober 2018. Keterbaharuan dalam penelitian ini adalah membandingkan dua model Autoregressive Integrated Moving Average (ARIMA) dan Generalized Autoregressive Conditional Heteroscedasticity (GARCH) sehingga dapat diketahui model yang memiliki tingkat keakuratan terbaik untuk meramalkan harga saham pada periode mendatang. Hasil dari penelitian ini menggambarkan bahwa terdapat unsur heterokedastisitas pada saham ADRO sehingga pemodelan tidak berhenti pada model ARIMA namun dilanjutkan sampai model GARCH. Sedangkan data saham PTBA tidak mengandung unsur heterokedastisitas sehingga pemodelan hanya sampai model ARIMA. Pada saham ADRO model ARIMA mempunyai tingkat kesalahan yang lebih kecil dibandingkan model GARCH. Pada saham PTBA model ARIMA juga terpilih sebagai model yang paling akurat. Kata Kunci: ARIMA, GARCH, dan Pertambangan

Long memory volatility in Asian stock markets

2017 ◽  
Vol 29 (3) ◽  
pp. 423-442 ◽  
Author(s):  
Geeta Duppati ◽  
Anoop S. Kumar ◽  
Frank Scrimgeour ◽  
Leon Li
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Realized Volatility ◽  
Theory And Practice ◽  
Long Term Memory ◽  
Conditional Heteroscedasticity ◽  
Term Memory ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory. Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory. Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts. Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management. Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks. Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

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