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# Judul Penulis Tahun Akses
Jurnal Institusi

GARCH-M MODEL AND THE BEHAVIOR OF RISK-RETURN RELATIONSHIP IN INDONESIA STOCK MARKET

Jurnal Ilmiah Ekonomi Dan Bisnis; Vol. 19 No. 2 (2022); 101-109
Universitas Lancang Kuning, 2022DOI: 10.31849/jieb.v19i2.6315Copyright (c) 2022 Masfar Gazali, Rahmadianti Thomas, Matrodji Mustafa
This study examines the risk-return trade-off and volatility behaviour in Indonesia stock market. As the analytical tool this study uses GARCH-M model with symmetric GARCH(1,1).  To obtain more reliable results, this study takes daily and we...
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Jurnal Institusi

PREDIKSI HARGA DAN VOLATILITAS EMAS DUNIA HARIAN: PERBANDINGAN MODEL GARCH DAN LONG SHORT-TERM MEMORY

ZONAsi: Jurnal Sistem Informasi; Vol. 7 No. 2 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Mei 2025; 466 - 475
Universitas Lancang Kuning, 2025DOI: 10.31849/zn.v7i2.26764https://creativecommons.org/licenses/by-sa/4.0
Penelitian ini bertujuan untuk membandingkan kinerja model Long Short-Term Memory (LSTM) dan Generalized Autoregressive Conditional Heteroskedasticity (GARCH) dalam memprediksi harga dan volatilitas emas harian. Data harga emas dunia periode 2018–2...
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PubMed

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

ScientificWorldJournal
Wiley, 2014DOI: 10.1155/2014/497941https://creativecommons.org/licenses/by/3.0/
The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type mode...
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PubMed

A network autoregressive model with GARCH effects and its applications

PLoS One
PLOS, 2021DOI: 10.1371/journal.pone.0255422https://creativecommons.org/licenses/by/4.0/
In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR mo...
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PubMed

Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction

PeerJ Comput Sci
PeerJ, Inc, 2024DOI: 10.7717/peerj-cs.1735https://creativecommons.org/licenses/by/4.0/
Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to s...
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PubMed

New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model

Heliyon
Elsevier, 2023DOI: 10.1016/j.heliyon.2023.e14017https://creativecommons.org/licenses/by-nc-nd/4.0/
In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained...
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PubMed

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

PLoS One
PLOS, 2024DOI: 10.1371/journal.pone.0297641https://creativecommons.org/licenses/by/4.0/
Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility ...
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PubMed

Risk contagion of COVID-19 to oil prices: A Markov switching GARCH and PCA approach

PLoS One
PLOS, 2024DOI: 10.1371/journal.pone.0312718https://creativecommons.org/licenses/by/4.0/
The COVID-19 pandemic and its impact on crude oil prices created additional risks throughout the financial industry. To contribute to the ongoing debates, this paper empirically examined the risk contagion of COVID-19 to oil prices by incorporating a...
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PubMed

Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect

Chaos Solitons Fractals
2021DOI: 10.1016/j.chaos.2021.111227© 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
COVID-19 pandemic has affected more than a hundred fifty million people and killed over three million people worldwide over the past year. During this period, different forecasting models have tried to forecast time path of COVID-19 pandemic. Unlike ...
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PubMed

Enhancing stock volatility prediction with the AO-GARCH-MIDAS model

PLoS One
PLOS, 2024DOI: 10.1371/journal.pone.0305420https://creativecommons.org/licenses/by/4.0/
Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse...
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PubMed

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model

PLoS One
PLOS, 2018DOI: 10.1371/journal.pone.0198753https://creativecommons.org/licenses/by/4.0/
In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student’s-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) m...
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PubMed

Modeling Saudi stock index returns and volatility: a dual approach using GARCH and neural networks

Front Artif Intell
Frontiers Media SA, 2026DOI: 10.3389/frai.2026.1714822https://creativecommons.org/licenses/by/4.0/
The financial markets are the drivers of economic growth as they organize savings, bring in foreign investment, and they efficiently allocate resources. The Tadawul is the largest stock market in the GCC, which is highly impacted by prices of oil and...
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PubMed

Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework

Entropy (Basel)
Multidisciplinary Digital Publishing Institute (MDPI), 2022DOI: 10.3390/e24101392https://creativecommons.org/licenses/by/4.0/
Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH)...
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PubMed

South African inflation modelling using bootstrapped long short-term memory methods

SN Bus Econ
2023DOI: 10.1007/s43546-023-00490-9© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Inflation is a critical economic series, and proper targeting is required for a stable economy. With the current economic conditions that the world has faced as a result of COVID-19, understanding the effects of this on economies is critical because ...
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PubMed

LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios

Comput Econ
2023DOI: 10.1007/s10614-023-10373-8© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity (GARCH) models, multilayer perceptron (MLP), long short-term memory (LSTM), and hybrid models of the t...
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PubMed

Study of the cross-market effects of Brexit based on the improved symbolic transfer entropy GARCH model—An empirical analysis of stock–bond correlations

PLoS One
PLOS, 2017DOI: 10.1371/journal.pone.0183194https://creativecommons.org/licenses/by/4.0/
In this paper, we study the cross-market effects of Brexit on the stock and bond markets of nine major countries in the world. By incorporating information theory, we introduce the time-varying impact weights based on symbolic transfer entropy to imp...
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PubMed

Estimation and tests for power-transformed and threshold GARCH models()

J Econom
2007DOI: 10.1016/j.jeconom.2007.06.004Copyright © 2007 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Consider a class of power-transformed and threshold GARCH [Formula: see text] (PTTGRACH [Formula: see text]) model, which is a natural generalization of power-transformed and threshold GARCH(1,1) model in Hwang and Basawa [2004. Stationarity and mome...
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PubMed

Symmetric and asymmetric GARCH estimations of the impact of oil price uncertainty on output growth: evidence from the G7

Lett Spat Resour Sci
2023DOI: 10.1007/s12076-023-00325-z© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Crude oil is an essential source of energy. Without access to energy, output growth is impossible. As a result of this link, volatility in oil prices has the ability to induce fluctuations in the output of both developed and developing economies. Mor...
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PubMed

Estimation of the parameters of symmetric stable ARMA and ARMA–GARCH models

J Appl Stat
Taylor & Francis, 2021DOI: 10.1080/02664763.2021.1928019© 2021 Informa UK Limited, trading as Taylor & Francis Group
In this article, we first propose the modified Hannan–Rissanen Method for estimating the parameters of autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedas...
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PubMed

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

Entropy (Basel)
Multidisciplinary Digital Publishing Institute (MDPI), 2021DOI: 10.3390/e23091151https://creativecommons.org/licenses/by/4.0/
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage eff...
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