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.

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 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 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.  

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.

scholarly journals Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

2021 ◽  
Vol 14 (7) ◽  
pp. 314
Author(s):  
Najam Iqbal ◽  
Muhammad Saqib Manzoor ◽  
Muhammad Ishaq Bhatti
Keyword(s):  
Stock Returns ◽  
Garch Model ◽  
Bad News ◽  
Good News ◽  
Fiscal Policies ◽  
News Shocks ◽  
Australian Health ◽  
Egarch Model ◽  
Negative Effect ◽  
The Garch Model

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

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.

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