Autoregressive Conditional Heteroscedasticity

2012 ◽  
pp. 281-310
Author(s):  
Gebhard Kirchgässner ◽  
Jürgen Wolters ◽  
Uwe Hassler
Keyword(s):  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

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

scholarly journals VOLATILITY SHOCK PERSISTENCE IN INVESTMENT DECISION MAKING: A COMPARISON BETWEEN THE CONSUMER GOODS AND PROPERTY-REAL ESTATE SECTORS OF THE INDONESIAN CAPITAL MARKET

2018 ◽  
Vol 33 (2) ◽  
pp. 112
Author(s):  
Ari Christianti
Keyword(s):  
Real Estate ◽  
Stock Returns ◽  
Consumer Goods ◽  
Investment Decision ◽  
Large Population ◽  
Conditional Heteroscedasticity ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Autoregressive Conditional Heteroscedasticity

Research about volatility shock persistence is very important, since it could reflect the risks that can be used to estimate the fluctuations of stock returns in the future. This paper investigates a comparison of the volatility shock persistence sectoral indexes between the consumer goods (CONS) and property-real estate (PROP) sectors, using a single index model analyzed using GARCH (Generalized Autoregressive Conditional Heteroscedasticity) and I-GARCH (Integrated-Generalized Autoregressive Conditional Heteroscedasticity). By using index return data from January 2010-December 2015, the research shows that CONS and PROP tend to produce the same results. The CONS and PROP indexes’ responses to volatility shocks tended to be quite fast. Hence, the single index model of the CONS and the PROP indexes can quickly return to its normal stability. It means that, in the presence of certain information which could affect the volatility of the return from these sectors, the market will respond and adapt immediately. This might be attributed to the fact that CONS is a sector that involves fast moving products. Furthermore, the PROP sector has an indirect effect by increasing the real sectoral economic activity and economic growth in Indonesia, which has a large population. Thus, it is recommended that investors who are risk averse and risk neutral should invest in these sectors, because the volatility of both indexes can be monitored based on the existing information.

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 Analisis Votalitas Saham di Jakarta Islamic Index (JII) periode Januari 2015-Januari 2018

2018 ◽  
Vol 1 (1) ◽  
pp. 147
Author(s):  
Hendri Tanjung ◽  
Taufik Akbar Martua Siregar
Keyword(s):  
Stock Exchange ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Penelitian ini bertujuan untuk melihat volatilitas Jakarta Islamic Index (JII) pada Jakarta Stock Exchange. Adapun teknik analisis yang digunakan pada penelitian ini adalah Generalized Autoregressive Conditional Heteroscedasticity (GARCH) dan Autoregressive Conditional Heteroscedasticity (ARCH). Kenormalan distribusi tingkat return pada JII dianalisis untuk menjawab apakah returnnya tersebar secara normal atau tidak. Dengan menggunakan data JII dari januari 2015 sampai dengan januari 2018 (724 data harian), ditemukan bahwa distribusi dari return JII tidak menyebar normal. Penelitian ini menyimpulkan bahwa return dari Jakarta Islamic Indeks sangat berfluktuasi.  Adapun implikasinya adalah akan diperoleh keuntungan yang sangat tinggi dan kerugian yang sangat besar pada satu hari.

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

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